13.4.1.1 Other Sparse Coding, Low Dimensional Representation, Invariants

Feng, J., Ni, B., Xu, D., Yan, S.,
Histogram Contextualization,
IP(21), No. 2, February 2012, pp. 778-788.
IEEE DOI 1201
Histogram loses the order. Technique to incoprorate spatial information. BibRef

Lui, Y.M.[Yui Man],
Advances in matrix manifolds for computer vision,
IVC(30), No. 6-7, June 2012, pp. 380-388.
Elsevier DOI 1206
Lie groups; Stiefel manifolds; Grassmann manifolds; Riemannian manifolds BibRef

Wang, J., Gong, Y.,
Discovering Image Semantics in Codebook Derivative Space,
MultMed(14), No. 4, 2012, pp. 986-994.
IEEE DOI 1208
Sparse coding. Locality-constraint linear coding. BibRef

Jing, L., Zhang, C., Ng, M.K.,
SNMFCA: Supervised NMF-Based Image Classification and Annotation,
IP(21), No. 11, November 2012, pp. 4508-4521.
IEEE DOI 1210
nonnegative matrix factorization. Classification and annotation. BibRef

Chen, X.[Xi], Zhang, J.S.[Jia-Shu], Li, D.F.[De-Fang],
Direct Discriminant Locality Preserving Projection With Hammerstein Polynomial Expansion,
IP(21), No. 12, December 2012, pp. 4858-4867.
IEEE DOI 1212
BibRef

Li, Q., Zhang, H., Guo, J., Bhanu, B., An, L.,
Reference-Based Scheme Combined With K-SVD for Scene Image Categorization,
SPLetters(20), No. 1, January 2013, pp. 67-70.
IEEE DOI 1212
Locality-constrained Linear Coding (LLC) features BibRef

Li, Q.[Qun], Xu, D.[Ding], An, L.[Le],
Discriminative Reference-Based Scene Image Categorization,
IEICE(E97-D), No. 10, October 2014, pp. 2823-2826.
WWW Link. 1411
BibRef

Duan, C.H.[Chih-Hsueh], Chiang, C.K.[Chen-Kuo], Lai, S.H.[Shang-Hong],
Face Verification With Local Sparse Representation,
SPLetters(20), No. 2, February 2013, pp. 177-180.
IEEE DOI 1302
BibRef

Tang, J.H.[Jin-Hui], Yan, S.C.[Shui-Cheng], Wright, J.[John], Tian, Q.[Qi], Pang, Y.W.[Yan-Wei], Pissaloux, E.[Edwige],
Sparse representations for image and video analysis,
JVCIR(24), No. 2, February 2013, pp. 93-94.
Elsevier DOI 1302
BibRef

Ricci, E.[Elisa], Zen, G.[Gloria], Sebe, N.[Nicu], Messelodi, S.,
A Prototype Learning Framework Using EMD: Application to Complex Scenes Analysis,
PAMI(35), No. 3, March 2013, pp. 513-526.
IEEE DOI 1303
BibRef

Zen, G.[Gloria], Ricci, E.[Elisa], Sebe, N.[Nicu],
Exploiting Sparse Representations for Robust Analysis of Noisy Complex Video Scenes,
ECCV12(VI: 199-213).
Springer DOI 1210
BibRef

Zen, G.[Gloria], Rostamzadeh, N.[Negar], Staiano, J.[Jacopo], Ricci, E.[Elisa], Sebe, N.[Nicu],
Enhanced semantic descriptors for functional scene categorization,
ICPR12(1985-1988).
WWW Link. 1302
BibRef

Ravishankar, S.[Saiprasad], Bresler, Y.[Yoram],
Learning Doubly Sparse Transforms for Images,
IP(22), No. 12, 2013, pp. 4598-4612.
IEEE DOI 1312
BibRef
Earlier:
Learning sparsifying transforms for image processing,
ICIP12(681-684).
IEEE DOI 1302
BibRef
And:
Learning doubly sparse transforms for image representation,
ICIP12(685-688).
IEEE DOI 1302
image denoising BibRef

Berthoumieu, Y., Dossal, C., Pustelnik, N., Ricoux, P., Turcu, F.,
An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices,
JMIV(48), No. 2, February 2014, pp. 266-278.
Springer DOI 1402
BibRef

Chen, Y.J.[Yun-Jin], Ranftl, R.[Rene], Pock, T.[Thomas],
Insights Into Analysis Operator Learning: From Patch-Based Sparse Models to Higher Order MRFs,
IP(23), No. 3, March 2014, pp. 1060-1072.
IEEE DOI 1403
image denoising BibRef

Bako, L.,
Subspace Clustering Through Parametric Representation and Sparse Optimization,
SPLetters(21), No. 3, March 2014, pp. 356-360.
IEEE DOI 1403
convex programming BibRef

Park, S.[Soonyong], Park, S.K.[Sung-Kee], Hebert, M.,
Fast and Scalable Approximate Spectral Matching for Higher Order Graph Matching,
PAMI(36), No. 3, March 2014, pp. 479-492.
IEEE DOI 1403
approximation theory. approximated affinity tensor. BibRef

Zhang, C.J.[Chun-Jie], Liu, J.[Jing], Liang, C.[Chao], Xue, Z.[Zhe], Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming],
Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition,
CVIU(123), No. 1, 2014, pp. 14-22.
Elsevier DOI 1405
Sparse coding BibRef

Zhang, C.J.[Chun-Jie], Liu, J.[Jing], Tian, Q.[Qi], Xu, C.S.[Chang-Sheng], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Image classification by non-negative sparse coding, low-rank and sparse decomposition,
CVPR11(1673-1680).
IEEE DOI 1106
BibRef

Zhang, T.Z.[Tian-Zhu], Ghanem, B.[Bernard], Liu, S.[Si], Xu, C.S.[Chang-Sheng], Ahuja, N.[Narendra],
Low-Rank Sparse Coding for Image Classification,
ICCV13(281-288)
IEEE DOI 1403
bow; image classification; low-rank
See also Robust Visual Tracking Via Consistent Low-Rank Sparse Learning. BibRef

Somasundaram, G.[Guruprasad], Cherian, A.[Anoop], Morellas, V.[Vassilios], Papanikolopoulos, N.[Nikolaos],
Action recognition using global spatio-temporal features derived from sparse representations,
CVIU(123), No. 1, 2014, pp. 1-13.
Elsevier DOI 1405
BibRef
Earlier: A1, A3, A4, Only:
Object classification with efficient global self-similarity descriptors based on sparse representations,
ICIP12(2165-2168).
IEEE DOI 1302
BibRef
And: A2, A3, A4, Only:
Robust Sparse Hashing,
ICIP12(2417-2420).
IEEE DOI 1302
Global spatio-temporal features BibRef

Cherian, A.[Anoop], Sra, S.[Suvrit], Morellas, V.[Vassilios], Papanikolopoulos, N.P.[Nikolaos P.],
Efficient Nearest Neighbors via Robust Sparse Hashing,
IP(23), No. 8, August 2014, pp. 3646-3655.
IEEE DOI 1408
cryptography
See also Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices. BibRef

Zheng, N.[Ning], Qi, L.[Lin], Guan, L.[Ling],
Generalized multiple maximum scatter difference feature extraction using QR decomposition,
JVCIR(25), No. 6, 2014, pp. 1460-1471.
Elsevier DOI 1407
Feature extraction BibRef

Zheng, N.[Ning], Guo, X.[Xin], Tie, Y.[Yun], Dong, N.[Nan], Qi, L.[Lin], Guan, L.[Ling],
Incremental generalized multiple maximum scatter difference with applications to feature extraction,
JVCIR(55), 2018, pp. 67-79.
Elsevier DOI 1809
Feature extraction, Generalized multiple maximum scatter difference, Incremental GMMSD+ BibRef

Zheng, N.[Ning], Qi, L.[Lin], Gao, L.[Lei], Guan, L.[Ling],
Generalized MMSD feature extraction using QR decomposition,
VCIP12(1-5).
IEEE DOI 1302
Multiple Maximum scatter difference. BibRef

Yang, W.K.[Wan-Kou], Wang, Z.Y.[Zhen-Yu], Sun, C.Y.[Chang-Yin],
A collaborative representation based projections method for feature extraction,
PR(48), No. 1, 2015, pp. 20-27.
Elsevier DOI 1410
Sparse representation BibRef

Jiang, R., Qiao, H., Zhang, B.,
Speeding Up Graph Regularized Sparse Coding by Dual Gradient Ascent,
SPLetters(22), No. 3, March 2015, pp. 313-317.
IEEE DOI 1410
Convergence BibRef

Su, Y.[Ya], Li, S., Wang, S.J.[Sheng-Jin], Fu, Y.[Yun],
Submanifold Decomposition,
CirSysVideo(24), No. 11, November 2014, pp. 1885-1897.
IEEE DOI 1411
BibRef
Earlier: A1, A3, A4, Only:
Submanifold decomposition,
ICPR12(1755-1758).
WWW Link. 1302
BibRef

Liu, Y.[Yang], Liu, C.Y.[Chen-Yu], Tang, Y.F.[Yu-Fang], Liu, H.[Haixu], Ouyang, S.X.[Shu-Xin], Li, X.M.[Xue-Ming],
Robust block sparse discriminative classification framework,
JOSA-A(31), No. 12, December 2014, pp. 2806-2813.
DOI Link 1412
Image processing; Pattern recognition; Machine vision; Algorithms Apply to texture and face recognition. BibRef

Liu, Y.[Yang], Li, X.M.[Xue-Ming], Liu, C.Y.[Chen-Yu], Liu, H.[Haixu],
Structure-Constrained Low-Rank and Partial Sparse Representation with Sample Selection for image classification,
PR(59), No. 1, 2016, pp. 5-13.
Elsevier DOI 1609
BibRef
Earlier: A1, A4, A3, A2:
Structure-constrained low-rank and partial sparse representation for image classification,
ICIP14(5222-5226)
IEEE DOI 1502
Sparse coding Accuracy BibRef

Xu, Y.[Yi], Yu, L.C.[Li-Cheng], Xu, H.T.[Hong-Teng], Zhang, H.[Hao], Nguyen, T.[Truong],
Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis,
IP(24), No. 4, April 2015, pp. 1315-1329.
IEEE DOI 1503
channel coding BibRef

Shen, X.Y.[Xin-Yue], Gu, Y.T.[Yuan-Tao],
Restricted Isometry Property of Subspace Projection Matrix Under Random Compression,
SPLetters(22), No. 9, September 2015, pp. 1326-1330.
IEEE DOI 1503
matrix algebra BibRef

Zhou, X.W.[Xiao-Wei], Yang, C.[Can], Zhao, H.Y.[Hong-Yu], Yu, W.C.[Wei-Chuan],
Low-Rank Modeling and Its Applications in Image Analysis,
Surveys(47), No. 2, January 2015, pp. Article No 36.
DOI Link 1503
Low-rank modeling generally refers to a class of methods that solves problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing, and bioinformatics. BibRef

Srinivas, U.[Umamahesh], Suo, Y.M.[Yuan-Ming], Dao, M.[Minh], Monga, V.[Vishal], Tran, T.D.[Trac D.],
Structured Sparse Priors for Image Classification,
IP(24), No. 6, June 2015, pp. 1763-1776.
IEEE DOI 1504
BibRef
Earlier: ICIP13(3211-3215)
IEEE DOI 1402
Class-specific priors Algorithm design and analysis. BibRef

Mousavi, H.S., Monga, V., Tran, T.D.,
Iterative Convex Refinement for Sparse Recovery,
SPLetters(22), No. 11, November 2015, pp. 1903-1907.
IEEE DOI 1509
Bayes methods BibRef

Suo, Y.M.[Yuan-Ming], Dao, M.[Minh], Tran, T.D.[Trac D.], Mousavi, H.S.[Hojjat S.], Srinivas, U.[Umamahesh], Monga, V.[Vishal],
Group structured dirty dictionary learning for classification,
ICIP14(150-154)
IEEE DOI 1502
Dictionaries BibRef

Mousavi, H.S.[Hojjat S.], Srinivas, U.[Umamahesh], Monga, V.[Vishal], Suo, Y.M.[Yuan-Ming], Dao, M.[Minh], Tran, T.D.[Trac D.],
Multi-task image classification via collaborative, hierarchical spike-and-slab priors,
ICIP14(4236-4240)
IEEE DOI 1502
Bayes methods BibRef

Zhi, R.C.[Rui-Cong], Zhao, L.[Lei], Shi, B.[Bolin], Jin, Y.[Yi],
Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition,
IEICE(E97-D), No. 9, September 2014, pp. 2434-2442.
WWW Link. 1410
BibRef

Yang, J.Y.[Jing-Yu], Gan, Z.Q.[Zi-Qiao], Wu, Z.Y.[Zhao-Yang], Hou, C.P.[Chun-Ping],
Estimation of Signal-Dependent Noise Level Function in Transform Domain via a Sparse Recovery Model,
IP(24), No. 5, May 2015, pp. 1561-1572.
IEEE DOI 1504
BibRef
Earlier: A1, A3, A4, Only:
Estimation of signal-dependent sensor noise via sparse representation of noise level functions,
ICIP12(673-676).
IEEE DOI 1302
discrete cosine transforms BibRef

Ülkü, I.[Irem], Töreyin, B.U.[Behçet Ugur],
Sparse coding of hyperspectral imagery using online learning,
SIViP(9), No. 4, May 2015, pp. 959-966.
Springer DOI 1504
BibRef

Gao, Q.X.[Quan-Xue], Huang, Y.F.[Yun-Fang], Zhang, H.L.[Hai-Lin], Hong, X.[Xin], Li, K.[Kui], Wang, Y.[Yong],
Discriminative sparsity preserving projections for image recognition,
PR(48), No. 8, 2015, pp. 2543-2553.
Elsevier DOI 1505
Dimensionality reduction BibRef

Hettiarachchi, R., Peters, J.F.,
Multi-manifold LLE learning in pattern recognition,
PR(48), No. 9, 2015, pp. 2947-2960.
Elsevier DOI 1506
Locally linear embedding. Multi-manifolds BibRef

Zhang, L.[Li], Leng, Y.Q.[Yi-Qin], Yang, J.W.[Ji-Wen], Li, F.Z.[Fan-Zhang],
Supervised locally linear embedding algorithm based on orthogonal matching pursuit,
IET-IPR(9), No. 8, 2015, pp. 626-633.
DOI Link 1506
image classification BibRef

Qian, L.Q.[Li-Qiang], Zhang, L.[Li], Bao, X.[Xing], Li, F.Z.[Fan-Zhang], Yang, J.W.[Ji-Wen],
Supervised sparse neighbourhood preserving embedding,
IET-IPR(11), No. 3, March 2017, pp. 190-199.
DOI Link 1703
BibRef

Wen, B.[Bihan], Ravishankar, S.[Saiprasad], Bresler, Y.[Yoram],
Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications,
IJCV(114), No. 2-3, September 2015, pp. 137-167.
Springer DOI 1509
BibRef
Earlier:
Learning overcomplete sparsifying transforms with block cosparsity,
ICIP14(803-807)
IEEE DOI 1502
Big data. Analytical models BibRef

Wen, B.[Bihan], Ravishankar, S.[Saiprasad], Bresler, Y.[Yoram],
Learning flipping and rotation invariant sparsifying transforms,
ICIP16(3857-3861)
IEEE DOI 1610
Clustering algorithms BibRef

Wen, B.[Bihan], Ravishankar, S.[Saiprasad], Bresler, Y.[Yoram],
VIDOSAT: High-Dimensional Sparsifying Transform Learning for Online Video Denoising,
IP(28), No. 4, April 2019, pp. 1691-1704.
IEEE DOI 1901
BibRef
Earlier:
Video denoising by online 3D sparsifying transform learning,
ICIP15(118-122)
IEEE DOI 1512
image denoising, image reconstruction, learning (artificial intelligence), transforms, video denoising BibRef

Ravishankar, S.[Saiprasad], Bresler, Y.[Yoram],
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to Magnetic Resonance Imaging,
SIIMS(8), No. 4, 2015, pp. 2519-2557.
DOI Link 1601
BibRef

Jahromi, M.N.S.[Mohammad N. S.], Salman, M.S.[Mohammad Shukri], Hocanin, A.[Aykut], Kukrer, O.[Osman],
Convergence analysis of the zero-attracting variable step-size LMS algorithm for sparse system identification,
SIViP(9), No. 6, September 2015, pp. 1353-1356.
Springer DOI 1509
BibRef

Eleyan, G.[Gülden], Salman, M.S.[Mohammad Shukri],
Convergence analysis of the mixed-norm LMS and two versions for sparse system identification,
SIViP(14), No. 5, July 2020, pp. 965-970.
Springer DOI 2006
BibRef

Jahromi, M.N.S.[Mohammad N. S.], Salman, M.S.[Mohammad Shukri], Hocanin, A.[Aykut], Kukrer, O.[Osman],
Mean-square deviation analysis of the zero-attracting variable step-size LMS algorithm,
SIViP(11), No. 3, March 2017, pp. 533-540.
Springer DOI 1702
BibRef

Aliyu, M.L.[Muhammad Lawan], Alkassim, M.A.[Mujahid Ado], Salman, M.S.[Mohammad Shukri],
A p-norm variable step-size LMS algorithm for sparse system identification,
SIViP(9), No. 7, October 2015, pp. 1559-1565.
Springer DOI 1509
BibRef

Xie, J.N.[Jia-Nwen], Hu, W.Z.[Wen-Ze], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Learning Sparse FRAME Models for Natural Image Patterns,
IJCV(114), No. 2-3, September 2015, pp. 91-112.
Springer DOI 1509
BibRef
Earlier:
Learning Inhomogeneous FRAME Models for Object Patterns,
CVPR14(1035-1042)
IEEE DOI 1409
Energy-based models FRAME (Filters, Random field, And Maximum Entropy). BibRef

Dai, J.F.[Ji-Feng], Hong, Y.[Yi], Hu, W.Z.[Wen-Ze], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models,
CVPR14(2505-2512)
IEEE DOI 1409
BibRef

Chabiron, O.[Olivier], Malgouyres, F.[François], Tourneret, J.Y.[Jean-Yves], Dobigeon, N.[Nicolas],
Toward Fast Transform Learning,
IJCV(114), No. 2-3, September 2015, pp. 195-216.
Springer DOI 1509
BibRef

Ji, J.Q.[Jian-Qiu], Li, J.M.[Jian-Min], Tian, Q.[Qi], Yan, S.C.[Shui-Cheng], Zhang, B.[Bo],
Angular-Similarity-Preserving Binary Signatures for Linear Subspaces,
IP(24), No. 11, November 2015, pp. 4372-4380.
IEEE DOI 1509
computer vision BibRef

Fukui, K., Maki, A.,
Difference Subspace and Its Generalization for Subspace-Based Methods,
PAMI(37), No. 11, November 2015, pp. 2164-2177.
IEEE DOI 1511
feature extraction BibRef

Camerlenghi, F.[Federico], Villa, E.[Elena],
Optimal Bandwidth of the Minkowski Content-Based Estimator of the Mean Density of Random Closed Sets: Theoretical Results and Numerical Experiments,
JMIV(53), No. 3, November 2015, pp. 264-287.
Springer DOI 1511
BibRef

Tao, J.[Jian_Wen], Wen, S.T.[Shi-Ting], Hu, W.J.[Wen-Jun],
Robust domain adaptation image classification via sparse and low rank representation,
JVCIR(33), No. 1, 2015, pp. 134-148.
Elsevier DOI 1512
Robust domain adaptation learning BibRef

Tao, J.W.[Jian-Wen], Song, D.W.[Da-Wei], Wen, S.T.[Shi-Ting], Hu, W.J.[Wen-Jun],
Robust multi-source adaptation visual classification using supervised low-rank representation,
PR(61), No. 1, 2017, pp. 47-65.
Elsevier DOI 1705
Multiple source domain adaptation BibRef

Li, X.[Xiao], Fang, M.[Min], Zhang, J.J.[Ju-Jie],
Projected Transfer Sparse Coding for cross domain image representation,
JVCIR(33), No. 1, 2015, pp. 265-272.
Elsevier DOI 1512
Image representation BibRef

Xu, Y.[Yong], Zhang, B.[Bob], Zhong, Z.F.[Zuo-Feng],
Multiple representations and sparse representation for image classification,
PRL(68, Part 1), No. 1, 2015, pp. 9-14.
Elsevier DOI 1512
Image classification BibRef

Kelly, P.A., Kibria, S.,
Complex Exponential Pseudomodes of LTI Operators Over Finite Intervals,
SPLetters(23), No. 1, January 2016, pp. 135-138.
IEEE DOI 1601
linear, time-invariant. Radar, ultrasound. Approximation methods BibRef

Zheng, H.X.[Hai-Xia], Ip, H.H.S.[Horace H.S.],
Image classification and annotation based on robust regularized coding,
SIViP(10), No. 1, January 2016, pp. 55-64.
Springer DOI 1601
Sparse coding. Geometric and local. BibRef

Li, P.[Ping], Bu, J.J.[Jia-Jun], Yu, J.[Jun], Chen, C.[Chun],
Towards robust subspace recovery via sparsity-constrained latent low-rank representation,
JVCIR(37), No. 1, 2016, pp. 46-52.
Elsevier DOI 1603
Latent low-rank representation BibRef

Karygianni, S.[Sofia], Frossard, P.[Pascal],
Sparse molecular image representation,
JVCIR(36), No. 1, 2016, pp. 213-228.
Elsevier DOI 1603
BibRef
And:
Learning from sparse codes,
ICIP16(3862-3866)
IEEE DOI 1610
Algorithm design and analysis BibRef

Yang, L.[Liu], Jing, L.P.[Li-Ping], Ng, M.K.[Michael K.], Yu, J.[Jian],
A discriminative and sparse topic model for image classification and annotation,
IVC(51), No. 1, 2016, pp. 22-35.
Elsevier DOI 1606
Graphical model BibRef

Larsson, V.[Viktor], Olsson, C.[Carl],
Convex Low Rank Approximation,
IJCV(120), No. 2, November 2016, pp. 194-214.
Springer DOI 1609
BibRef
Earlier:
Convex Envelopes for Low Rank Approximation,
EMMCVPR15(1-14).
Springer DOI 1504
BibRef

Larsson, V.[Viktor], Olsson, C.[Carl], Kahl, F.[Fredrik],
A Simple Method for Subspace Estimation with Corrupted Columns,
RSL-CV15(841-849)
IEEE DOI 1602
Closed-form solutions BibRef

Olsson, C., Carlsson, M., Bylow, E.,
A Non-convex Relaxation for Fixed-Rank Approximation,
RSL-CV17(1809-1817)
IEEE DOI 1802
Level set, Noise measurement. BibRef

Bylow, E.[Erik], Olsson, C.[Carl], Kahl, F.[Fredrik], Nilsson, M.,
Minimizing the Maximal Rank,
CVPR16(5887-5895)
IEEE DOI 1612
BibRef

Larsson, V.[Viktor], Olsson, C.[Carl], Bylow, E.[Erik], Kahl, F.[Fredrik],
Rank Minimization with Structured Data Patterns,
ECCV14(III: 250-265).
Springer DOI 1408
BibRef

Kim, E.[Eunwoo], Lee, M.[Minsik], Oh, S.H.[Song-Hwai],
Robust Elastic-Net Subspace Representation,
IP(25), No. 9, September 2016, pp. 4245-4259.
IEEE DOI 1609
data structures BibRef

Gao, S.H.[Sheng-Hua], Zeng, Z.N.[Zi-Nan], Jia, K.[Kui], Chan, T.H.[Tsung-Han], Tang, J.H.[Jin-Hui],
Patch-Set-Based Representation for Alignment-Free Image Set Classification,
CirSysVideo(26), No. 9, September 2016, pp. 1646-1658.
IEEE DOI 1609
Accuracy. sparse representation. BibRef

Ji, H.K.[Hong-Kun], Sun, Q.S.[Quan-Sen], Yuan, Y.H.[Yun-Hao], Ji, Z.X.[Ze-Xuan],
C2DMCP: View-consistent collaborative discriminative multiset correlation projection for data representation,
JVCIR(40, Part B), No. 1, 2016, pp. 393-405.
Elsevier DOI 1610
Multiset correlation projection Multi-view joint dimensionality reduction by maximizing linear correlations among the projections. BibRef

Shen, X.B.[Xiao-Bo], Yuan, Y.H.[Yun-Hao], Shen, F.M.[Fu-Min], Xu, Y.[Yang], Sun, Q.S.[Quan-Sen],
A novel multi-view dimensionality reduction and recognition framework with applications to face recognition,
JVCIR(53), 2018, pp. 161-170.
Elsevier DOI 1805
Multi-view learning, Canonical correlations, Dimensionality reduction, Face recognition BibRef

Wang, Q.Q.[Qian-Qian], Ma, L.[Lan], Gao, Q.X.[Quan-Xue], Li, Y.S.[Yun-Song], Huang, Y.F.[Yun-Fang], Liu, Y.[Yang],
Adaptive maximum margin analysis for image recognition,
PR(61), No. 1, 2017, pp. 339-347.
Elsevier DOI 1609
Maximum margin BibRef

Zhan, Y.Z.[Yong-Zhao], Liu, J.[Junqi], Gou, J.P.[Jian-Ping], Wang, M.C.[Min-Chao],
A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN,
JVCIR(41), No. 1, 2016, pp. 65-73.
Elsevier DOI 1612
Locality-sensitive discriminant sparse representation method (LSDSR). BibRef

Zhang, S.Z.[Shi-Zhou], Wang, J.J.[Jin-Jun], Tao, X.Y.[Xiao-Yu], Gong, Y.H.[Yi-Hong], Zheng, N.N.[Nan-Ning],
Constructing Deep Sparse Coding Network for image classification,
PR(64), No. 1, 2017, pp. 130-140.
Elsevier DOI 1701
Sparse Coding BibRef

Luu, K.[Khoa], Savvides, M.[Marios], Bui, T.D.[Tien D.], Suen, C.Y.[Ching Y.],
Compressed Submanifold Multifactor Analysis,
PAMI(39), No. 3, March 2017, pp. 444-456.
IEEE DOI 1702
BibRef
Earlier:
Compressed Submanifold Multifactor Analysis with adaptive factor structures,
ICPR12(2715-2718).
WWW Link. 1302
Approximation algorithms BibRef

Hsaio, W.H.[Wen-Hoar], Liu, C.L.[Chien-Liang], Wu, W.L.[Wei-Liang],
Locality-constrained max-margin sparse coding,
PR(65), No. 1, 2017, pp. 285-295.
Elsevier DOI 1702
Locality BibRef

Lim, K.L., Wang, H.,
Sparse Coding Based Fisher Vector Using a Bayesian Approach,
SPLetters(24), No. 1, January 2017, pp. 91-95.
IEEE DOI 1702
BibRef
And: Corrections: SPLetters(24), No. 4, April 2017, pp. 520-520.
IEEE DOI 1704
Encoding; Signal processing algorithms; Sparse matrices. Gaussian distribution BibRef

Sankaran, A.[Anush], Vatsa, M.[Mayank], Singh, R.[Richa], Majumdar, A.[Angshul],
Group sparse autoencoder,
IVC(60), No. 1, 2017, pp. 64-74.
Elsevier DOI 1704
Supervised autoencoder BibRef

Yadav, S., Singh, M., Vatsa, M.[Mayank], Singh, R.[Richa], Majumdar, A.[Angshul],
Low rank group sparse representation based classifier for pose variation,
ICIP16(2986-2990)
IEEE DOI 1610
Classification algorithms BibRef

Liu, S.G.[Shi-Gang], Li, L.J.[Ling-Jun], Peng, Y.[Yali], Qiu, G.Y.[Guo-Yong], Lei, T.[Tao],
Improved sparse representation method for image classification,
IET-CV(11), No. 4, June 2017, pp. 319-330.
DOI Link 1705
BibRef

Peng, Y.[Yali], Li, L.J.[Ling-Jun], Liu, S.G.[Shi-Gang], Li, J.[Jun], Cao, H.[Han],
Virtual samples and sparse representation-based classification algorithm for face recognition,
IET-CV(13), No. 2, March 2019, pp. 172-177.
DOI Link 1902
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Peng, Y.[Yali], Li, L.J.[Ling-Jun], Liu, S.G.[Shi-Gang], Li, J.[Jun], Wang, X.L.[Xi-Li],
Extended sparse representation-based classification method for face recognition,
RealTimeIP(14), No. 1, January 2018, pp. 991-1007.
WWW Link. 1809
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Liao, Y.Y.[Yi-Yi], Wang, Y.[Yue], Liu, Y.[Yong],
Graph Regularized Auto-Encoders for Image Representation,
IP(26), No. 6, June 2017, pp. 2839-2852.
IEEE DOI 1705
Jacobian matrices, graph theory, image classification, image coding, image representation, learning (artificial intelligence), pattern clustering, GAE, Jacobian matrix, clustering, complex modeling, deep architectures, deep representation learning techniques, encoder mapping, encoding model, graph regularized autoencoders, hidden representation space, high-dimensional input space, image classification, image clustering, image representation, intrinsic low-dimensional manifold, local invariant deep nonlinear mapping algorithm, manifold learning, weight matrix, weighted Frobenius norm, Algorithm design and analysis, Decoding, Image reconstruction, Image representation, Inference algorithms, Jacobian matrices, Manifolds, Auto-encoders, graph regularization, local, invariance BibRef

Eftekhari, A., Balzano, L., Wakin, M.B.,
What to Expect When You Are Expecting on the Grassmannian,
SPLetters(24), No. 6, June 2017, pp. 872-876.
IEEE DOI 1705
Coherence, Estimation, Partitioning algorithms, Q measurement, Signal processing algorithms, Size measurement, Standards, Fréchet expectation, Grassmannian averaging, matrix completion, principal component analysis, streaming algorithms, subspace identification BibRef

Zhang, Y.[Yupei], Xiang, M.[Ming], Yang, B.[Bo],
Low-rank preserving embedding,
PR(70), No. 1, 2017, pp. 112-125.
Elsevier DOI 1706
Low-rank, representation BibRef

Shu, Z.Q.[Zhen-Qiu], Fan, H.F.[Hong-Fei], Huang, P.[Pu], Wu, D.[Dong], Ye, F.Y.[Fei-Yue], Wu, X.J.[Xiao-Jun],
Multiple Laplacian graph regularised low-rank representation with application to image representation,
IET-IPR(11), No. 6, June 2017, pp. 370-378.
DOI Link 1706
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Qiao, X.[Xu], Liu, X.Q.[Xiao-Qing], Chen, Y.W.[Yen-Wei], Liu, Z.P.[Zhi-Ping],
Multi-dimensional data representation using linear tensor coding,
IET-IPR(11), No. 7, July 2017, pp. 492-501.
DOI Link 1707
BibRef

Yao, C.[Chao], Liu, Y.F.[Ya-Feng], Jiang, B.[Bo], Han, J.G.[Jun-Gong], Han, J.W.[Jun-Wei],
LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition,
IP(26), No. 11, November 2017, pp. 5257-5269.
IEEE DOI 1709
feature selection, handwriting recognition, face image classification, handwriting digits data set, Correlation, BibRef

Ji, R., Liu, H., Cao, L., Liu, D., Wu, Y., Huang, F.,
Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding,
IP(26), No. 11, November 2017, pp. 5411-5420.
IEEE DOI 1709
Acceleration, Binary codes, Image reconstruction, Manifolds, Matrix decomposition, Optimization, Discrete locally linear embedding, hashing, manifold learning, visual search BibRef

Liu, H., Ji, R., Wu, Y., Huang, F., Zhang, B.,
Cross-Modality Binary Code Learning via Fusion Similarity Hashing,
CVPR17(6345-6353)
IEEE DOI 1711
Atmospheric measurements, Benchmark testing, Binary codes, Matrix decomposition, Particle measurements, Symmetric matrices, Training BibRef

Gu, J.[Jing], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Wang, R.F.[Rong-Fang], Chen, P.[Puhua], Cui, Y.H.[Yuan-Hao], Xie, J.H.[Jun-Hu], Zhang, Y.[Yake],
Random subspace based ensemble sparse representation,
PR(74), No. 1, 2018, pp. 544-555.
Elsevier DOI 1711
Random subspace BibRef

Yu, X.[Xiyu], Liu, T.L.[Tong-Liang], Wang, X.C.[Xin-Chao], Tao, D.C.[Da-Cheng],
On Compressing Deep Models by Low Rank and Sparse Decomposition,
CVPR17(67-76)
IEEE DOI 1711
Computational modeling, Data models, Image reconstruction, Matrix decomposition, Neurons, Redundancy, Sparse, matrices BibRef

Wang, B.Y.[Bo-Yue], Hu, Y.L.[Yong-Li], Gao, J.B.[Jun-Bin], Ali, M.[Muhammad], Tien, D.[David], Sun, Y.F.[Yan-Feng], Yin, B.C.[Bao-Cai],
Low Rank Representation on SPD matrices with Log-Euclidean metric,
PR(76), No. 1, 2018, pp. 623-634.
Elsevier DOI 1801
BibRef
Earlier: A1, A2, A3, A6, A7, Only:
Low Rank Representation on Grassmann Manifolds,
ACCV14(I: 81-96).
Springer DOI 1504
Symmetrical positive definite matrices BibRef

Piao, X.L.[Xing-Lin], Hu, Y.L.[Yong-Li], Gao, J.B.[Jun-Bin], Sun, Y.F.[Yan-Feng], Yang, X.[Xin], Yin, B.C.[Bao-Cai],
A Spectral Clustering on Grassmann Manifold via Double Low Rank Constraint,
ICPR21(9392-9398)
IEEE DOI 2105
BibRef
Earlier: A1, A2, A3, A4, A6, Only:
Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering,
CVPR19(12067-12076).
IEEE DOI 2002
Manifolds, Measurement, Clustering methods, Machine learning, Euclidean distance, Data models, Data mining. BibRef

Chen, B.H.[Bo-Heng], Li, J.[Jie], Wei, G.[Gang], Ma, B.[Biyun],
A novel localized and second order feature coding network for image recognition,
PR(76), No. 1, 2018, pp. 339-348.
Elsevier DOI 1801
BibRef
Earlier: A1, A2, A4, A3:
Convolutional sparse coding classification model for image classification,
ICIP16(1918-1922)
IEEE DOI 1610
Deep neural network. Convolution BibRef

Osher, S.J.[Stanley J.], Shi, Z.Q.[Zuo-Qiang], Zhu, W.[Wei],
Low Dimensional Manifold Model for Image Processing,
SIIMS(10), No. 4, 2017, pp. 1669-1690.
DOI Link 1801
BibRef

Xue, S.K.[Sheng-Ke], Jin, X.Y.[Xin-Yu],
Robust classwise and projective low-rank representation for image classification,
SIViP(12), No. 1, January 2018, pp. 107-115.
Springer DOI 1801
BibRef

Hu, X., Heide, F., Dai, Q., Wetzstein, G.,
Convolutional Sparse Coding for RGB-NIR Imaging,
IP(27), No. 4, April 2018, pp. 1611-1625.
IEEE DOI 1802
filtering theory, image coding, image colour analysis, image denoising, image representation, structured illumination BibRef

Choudhury, B., Swanson, R., Heide, F., Wetzstein, G., Heidrich, W.,
Consensus Convolutional Sparse Coding,
ICCV17(4290-4298)
IEEE DOI 1802
image coding, image colour analysis, image reconstruction, image representation, image resolution, BibRef

Lu, X.H.[Xiao-Huan], He, Z.Y.[Zhen-Yu], Yi, S.Y.[Shuang-Yan], Chen, W.S.[Wen-Sheng],
Joint of locality- and globality-preserving projections,
SIViP(12), No. 3, March 2018, pp. 565-572.
WWW Link. 1804
Preserve global and local structure in sparse, lower dimensional representation. BibRef

Zhang, X., Sun, J., Ma, S., Lin, Z., Zhang, J., Wang, S., Gao, W.,
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding,
IP(27), No. 8, August 2018, pp. 3753-3765.
IEEE DOI 1806
Gaussian distribution, concave programming, data compression, entropy, image coding, image representation, alternating direction method of multipliers BibRef

Hosny, K.M.[Khalid M.], Darwish, M.M.[Mohamed M.],
New Set of Quaternion Moments for Color Images Representation and Recognition,
JMIV(60), No. 5, June 2018, pp. 717-736.
Springer DOI 1806
BibRef

Hosny, K.M.[Khalid M.], Darwish, M.M.[Mohamed M.],
New set of multi-channel orthogonal moments for color image representation and recognition,
PR(88), 2019, pp. 153-173.
Elsevier DOI 1901
Multi-channel orthogonal moments, Quaternion orthogonal moments, Chebyshev rational moments, Recognition rates BibRef

Hassan, G.[Gaber], Hosny, K.M.[Khalid M.], Farouk, R.M., Alzohairy, A.M.[Ahmed M.],
New Set of Invariant Quaternion Krawtchouk Moments for Color Image Representation and Recognition,
IJIG(22), No. 4, July 2022, pp. 2250037.
DOI Link 2208
BibRef

Xie, Q.[Qi], Zhao, Q.[Qian], Meng, D.Y.[De-Yu], Xu, Z.B.[Zong-Ben],
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery,
PAMI(40), No. 8, August 2018, pp. 1888-1902.
IEEE DOI 1807
Adaptation models, Algorithm design and analysis, Analytical models, Correlation, Minimization, Noise reduction, tucker decomposition BibRef

Hong, W.X.[Wei-Xiang], Yuan, J.S.[Jun-Song],
Fried Binary Embedding: From High-Dimensional Visual Features to High-Dimensional Binary Codes,
IP(27), No. 10, October 2018, pp. 4825-4837.
IEEE DOI 1808
binary codes, computational complexity, data visualisation, feature extraction, image coding, image retrieval, image retrieval BibRef

Hong, W.X.[Wei-Xiang], Yuan, J.S.[Jun-Song], Bhattacharjee, S.D.[Sreyasee Das],
Fried Binary Embedding for High-Dimensional Visual Features,
CVPR17(6221-6229)
IEEE DOI 1711
Binary codes, Encoding, Matrix decomposition, Optimization, Sparse matrices, Transforms, Visualization BibRef

Dornaika, F., El Traboulsi, Y.,
Proposals for local basis selection for the sparse representation-based classifier,
SIViP(12), No. 8, November 2018, pp. 1595-1601.
WWW Link. 1809
BibRef

Wang, B., Hu, Y., Gao, J., Sun, Y., Yin, B.,
Localized LRR on Grassmann Manifold: An Extrinsic View,
CirSysVideo(28), No. 10, October 2018, pp. 2524-2536.
IEEE DOI 1811
LRR: Low-rank representation. Manifolds, Symmetric matrices, Clustering algorithms, Data models, geodesic distance BibRef

Yi, S., He, Z., Cheung, Y., Chen, W.,
Unified Sparse Subspace Learning via Self-Contained Regression,
CirSysVideo(28), No. 10, October 2018, pp. 2537-2550.
IEEE DOI 1811
Principal component analysis, Eigenvalues and eigenfunctions, Robustness, Sparse matrices, Learning systems, sparse subspace learning BibRef

Wang, W.W.[Wei-Wei], Zhang, B.B.[Bin-Bin], Feng, X.C.[Xiang-Chu],
Subspace Segmentation by Correlation Adaptive Regression,
CirSysVideo(28), No. 10, October 2018, pp. 2612-2621.
IEEE DOI 1811
Correlation, Automobiles, Sparse matrices, Adaptation models, Data models, Motion segmentation, Noise measurement, grouping effect BibRef

Dong, X.[Xiao], Zhang, H.X.[Hua-Xiang], Zhu, L.[Lei], Wan, W.B.[Wen-Bo], Wang, Z.H.[Zhen-Hua], Wang, Q.A.[Qi-Ang], Guo, P.L.[Pei-Lian], Ji, H.[Hui], Sun, J.D.[Jian-De],
Weighted locality collaborative representation based on sparse subspace,
JVCIR(58), 2019, pp. 187-194.
Elsevier DOI 1901
Collaborative representation, Sparse subspace, Linear representation, Face recognition BibRef

Yin, W.B.[Wei-Bin], Xu, D.S.[Dong-Sheng], Wang, Z.[Zheng], Zhao, Z.J.[Zhi-Jun], Chen, C.[Chao], Yao, Y.Y.[Yi-Yang],
Perceptually learning multi-view sparse representation for scene categorization,
JVCIR(60), 2019, pp. 59-63.
Elsevier DOI 1903
Scene categorization, Scenery images. BibRef

Plaut, E.[Elad], Giryes, R.[Raja],
A Greedy Approach to Ll_(0,inf)-Based Convolutional Sparse Coding,
SIIMS(12), No. 1, 2019, pp. 186-210.
DOI Link 1904
BibRef

Teng, D.[Dan], Chu, D.[Delin],
A Fast Frequent Directions Algorithm for Low Rank Approximation,
PAMI(41), No. 6, June 2019, pp. 1279-1293.
IEEE DOI 1905
Approximation algorithms, Computational efficiency, Sparse matrices, Matrix decomposition, sparse subspace embedding BibRef

Shikhaliev, A.P., Potter, L.C., Chi, Y.,
Low-Rank Structured Covariance Matrix Estimation,
SPLetters(26), No. 5, May 2019, pp. 700-704.
IEEE DOI 1905
covariance matrices, eigenvalues and eigenfunctions, maximum likelihood estimation, optimisation, low rank BibRef

Li, X.R.[Xiang-Rui], Wang, A.D.[An-Dong], Lu, J.F.[Jian-Feng], Tang, Z.M.[Zhen-Min],
Statistical performance of convex low-rank and sparse tensor recovery,
PR(93), 2019, pp. 193-203.
Elsevier DOI 1906
Tensor recovery, Statistical performance, Tucker rank, Tensor de-noising, Tensor compressive sensing BibRef

Zhang, S.Z.[Shi-Zhou], Wang, J.J.[Jin-Jun], Shi, W.W.[Wei-Wei], Gong, Y.H.[Yi-Hong], Xia, Y.[Yong], Zhang, Y.N.[Yan-Ning],
Normalized Non-Negative Sparse Encoder for Fast Image Representation,
CirSysVideo(29), No. 7, July 2019, pp. 1962-1972.
IEEE DOI 1907
Encoding, Image coding, Feature extraction, Computational modeling, Neural networks, Image reconstruction, Training, Sparse coding, shift-invariant constraint BibRef

Kim, E.[Eunwoo], Lee, M.[Minsik], Oh, S.H.[Song-Hwai],
A Scalable Framework for Data-Driven Subspace Representation and Clustering,
PRL(125), 2019, pp. 742-749.
Elsevier DOI 1909
BibRef

Yan, J.W.[Jing-Wen], Chen, H.D.[Hong-Da], Zhai, Y.K.[Yi-Kui], Liu, Y.N.[Yi-Nan], Liu, L.[Lei],
Region-Division-Based Joint Sparse Representation Classification for Hyperspectral Images,
IET-IPR(13), No. 10, 22 August 2019, pp. 1694-1704.
DOI Link 1909
BibRef

de Souza, L.S.[Lincon Sales], Gatto, B.B.[Bernardo B.], Xue, J.H.[Jing-Hao], Fukui, K.[Kazuhiro],
Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition,
PR(97), 2020, pp. 107028.
Elsevier DOI 1910
Enhanced GDA, Randomized time warping, Motion recognition BibRef

de Souza, L.S., Bentes Gatto, B., Fukui, K.,
Enhancing discriminability of randomized time warping for motion recognition,
MVA17(77-80)
DOI Link 1708
Covariance matrices, Databases, Image sequences, Kernel, Manifolds, Principal component analysis, Training BibRef

de Souza, L.S.[Lincon Sales], Hino, H.[Hideitsu], Fukui, K.[Kazuhiro],
3D Object Recognition with Enhanced Grassmann Discriminant Analysis,
HIS16(III: 345-359).
Springer DOI 1704
BibRef

Huang, Y., Liao, G., Xiang, Y., Zhang, L., Li, J., Nehorai, A.,
Low-Rank Approximation via Generalized Reweighted Iterative Nuclear and Frobenius Norms,
IP(29), 2020, pp. 2244-2257.
IEEE DOI 2001
Minimization, Sparse matrices, Approximation algorithms, Iterative algorithms, Matrix decomposition, Image decomposition, generalized iterative reweighted Frobenius norm (GIRFN) BibRef

Xiao, X., Chen, Y., Gong, Y., Zhou, Y.,
2D Quaternion Sparse Discriminant Analysis,
IP(29), 2020, pp. 2271-2286.
IEEE DOI 2001
Quaternions, Feature extraction, Dimensionality reduction, Training, Correlation, Covariance matrices, RGB-D image BibRef

Lou, J., Cheung, Y.,
Robust Low-Rank Tensor Minimization via a New Tensor Spectral k-Support Norm,
IP(29), 2020, pp. 2314-2327.
IEEE DOI 2001
Optimization, Computational modeling, Minimization, Computer science, Task analysis, conditional gradient descent BibRef

Wang, Y., Kwok, J.T., Ni, L.M.,
Generalized Convolutional Sparse Coding With Unknown Noise,
IP(29), 2020, pp. 5386-5395.
IEEE DOI 2004
Convolutional sparse coding, noise modeling, Gaussian mixture model BibRef

Tian, Z.B.[Zhi-Bao], Zhang, H.[Hui], Chen, Y.[Yong], Zhang, D.[Dell],
Unsupervised hashing based on the recovery of subspace structures,
PR(103), 2020, pp. 107261.
Elsevier DOI 2005
Semantic hashing, Subspace learning, Low-rank representation, Discrete optimization BibRef

Azghani, M., Esmaeili, A., Behdin, K., Marvasti, F.,
Missing Low-Rank and Sparse Decomposition Based on Smoothed Nuclear Norm,
CirSysVideo(30), No. 6, June 2020, pp. 1550-1558.
IEEE DOI 2006
Sparse matrices, Matrix decomposition, Minimization, Lagrangian functions, Computational complexity, missing observation BibRef

Geng, X.R.[Xiu-Rui], Wang, L.[Lei],
NPSA: Nonorthogonal Principal Skewness Analysis,
IP(29), 2020, pp. 6396-6408.
IEEE DOI 2007
Tensile stress, Feature extraction, Principal component analysis, Indexes, Search problems, Covariance matrices, subspace BibRef

Chen, L., Jiang, X., Liu, X., Zhou, Z.,
Robust Low-Rank Tensor Recovery via Nonconvex Singular Value Minimization,
IP(29), 2020, pp. 9044-9059.
IEEE DOI 2009
Tensile stress, Robustness, Optimization, Minimization, Signal processing algorithms, Principal component analysis, nonconvex optimization BibRef

Wang, Y., Kou, K.I., Zou, C., Tang, Y.Y.,
Robust Sparse Representation in Quaternion Space,
IP(30), 2021, pp. 3637-3649.
IEEE DOI 2103
Quaternions, Color, Signal processing algorithms, Matching pursuit algorithms, Image color analysis, Image coding, color image processing BibRef

Tan, J.[Jin], Zhang, T.P.[Tai-Ping], Zhao, L.C.[Lin-Chang], Luo, X.L.[Xiao-Liu], Tang, Y.Y.[Yuan Yan],
A robust image representation method against illumination and occlusion variations,
IVC(112), 2021, pp. 104212.
Elsevier DOI 2107
Geometrical sparse representation. Geometrical structure, Sparse coding, Illumination analysis, Occlusion BibRef

Zhang, X.[Xiang], Ma, S.W.[Si-Wei], Wang, S.Q.[Shi-Qi], Zhang, J.[Jian], Sun, H.F.[Hui-Fang], Gao, W.[Wen],
Divisively Normalized Sparse Coding: Toward Perceptual Visual Signal Representation,
Cyber(51), No. 8, August 2021, pp. 4237-4250.
IEEE DOI 2108
Image coding, Visualization, Encoding, Indexes, Dictionaries, Image quality, Matching pursuit algorithms, visual perception BibRef

Wang, B.[Bin], Niu, H.F.[Hui-Fang], Zeng, J.C.[Jian-Chao], Bai, G.F.[Gui-Feng], Lin, S.[Suzhen], Wang, Y.B.[Yan-Bo],
Latent Representation Learning Model for Multi-Band Images Fusion via Low-Rank and Sparse Embedding,
MultMed(23), 2021, pp. 3137-3152.
IEEE DOI 2109
Image fusion, Feature extraction, Dictionaries, Sparse matrices, Machine learning, Neural networks, Tensile stress, Image fusion, representation learning BibRef

Mohideen, R.M.K.[Rahul Mohideen Kaja], Peter, P.[Pascal], Weickert, J.[Joachim],
A systematic evaluation of coding strategies for sparse binary images,
SP:IC(99), 2021, pp. 116424.
Elsevier DOI 2111
Compression, Sparse matrix, Binary image, Context mixing, Inpainting BibRef

Mookambiga, A., Gomathi, V.,
Kernel eigenmaps based multiscale sparse model for hyperspectral image classification,
SP:IC(99), 2021, pp. 116416.
Elsevier DOI 2111
Adaptive sparse representation, Schroedinger eigen maps, Spatial-spectral? features, Hyperspectral image classification BibRef

Veshki, F.G.[Farshad G.], Vorobyov, S.A.[Sergiy A.],
Efficient ADMM-Based Algorithms for Convolutional Sparse Coding,
SPLetters(29), 2022, pp. 389-393.
IEEE DOI 2202
Convolution, Signal processing algorithms, Dictionaries, Convolutional codes, Approximation algorithms, Optimization, alternating direction method of multipliers BibRef

Shen, Q.Q.[Qiang-Qiang], Liang, Y.S.[Yong-Sheng], Yi, S.[Shuangyan], Zhao, J.[Jiaoyan],
Fast Universal Low Rank Representation,
CirSysVideo(32), No. 3, March 2022, pp. 1262-1272.
IEEE DOI 2203
Minimization, Linear programming, Null space, Linear matrix inequalities, Data models, Solids, optimal mean BibRef

Mazarguil, A.[Antoine], Oudre, L.[Laurent], Vayatis, N.[Nicolas],
An Uncertainty Principle for Lowband Graph Signals,
SPLetters(29), 2022, pp. 727-731.
IEEE DOI 2204
Uncertainty, Laplace equations, Eigenvalues and eigenfunctions, Superluminescent diodes, Noise reduction, lowband graph signals BibRef

Xu, S.X.[Si-Xiang], Muselet, D.[Damien], Trémeau, A.[Alain],
Sparse coding and normalization for deep Fisher score representation,
CVIU(220), 2022, pp. 103436.
Elsevier DOI 2206
BibRef
Earlier:
Deep Fisher Score Representation via Sparse Coding,
CAIP21(II:412-421).
Springer DOI 2112
Fisher score, Sparse coding, Orderless pooling, Square root normalization, Classification BibRef

Liu, M.M.[Mei-Mei], Shang, Z.F.[Zuo-Feng], Yang, Y.[Yun], Cheng, G.[Guang],
Nonparametric Testing Under Randomized Sketching,
PAMI(44), No. 8, August 2022, pp. 4280-4290.
IEEE DOI 2207
Testing, Kernel, Smoothing methods, Computational modeling, Estimation, Eigenvalues and eigenfunctions, Upper bound, random sketch BibRef

Zhang, X.Q.[Xiao-Qian], Tan, Z.[Zhen], Sun, H.J.[Huai-Jiang], Wang, Z.G.[Zun-Gang], Qin, M.W.[Ming-Wei],
Orthogonal Low-Rank Projection Learning for Robust Image Feature Extraction,
MultMed(24), 2022, pp. 3882-3895.
IEEE DOI 2208
Feature extraction, Robustness, Data models, Principal component analysis, Dimensionality reduction, projection learning BibRef

Lu, Y.[Yuwu], Wang, W.J.[Wen-Jing], Zeng, B.Q.[Bi-Qing], Lai, Z.H.[Zhi-Hui], Shen, L.L.[Lin-Lin], Li, X.L.[Xue-Long],
Canonical Correlation Analysis With Low-Rank Learning for Image Representation,
IP(31), 2022, pp. 7048-7062.
IEEE DOI 2212
Correlation, Feature extraction, Principal component analysis, Training, Kernel, Noise measurement, Learning systems, image representation BibRef

Bruhin, N.D.[Nina Dekoninck], Davies, B.[Bryn],
Bioinspired Random Projections for Robust, Sparse Classification,
SIIMS(15), No. 4, 2022, pp. 1833-1850.
DOI Link 2212
BibRef

Fukui, K.[Kazuhiro], Sogi, N.[Naoya], Kobayashi, T.[Takumi], Xue, J.H.[Jing-Hao], Maki, A.[Atsuto],
Discriminant Feature Extraction by Generalized Difference Subspace,
PAMI(45), No. 2, February 2023, pp. 1618-1635.
IEEE DOI 2301
Principal component analysis, Image recognition, Feature extraction, Kernel, Lighting, Face recognition, PCA without data centering BibRef

Yang, Z.X.[Zi-Xiang], Shen, Q.[Qing], Liu, W.[Wei], Cui, W.[Wei],
A Sum-Difference Expansion Scheme for Sparse Array Construction Based on the Fourth-Order Difference Co-Array,
SPLetters(29), 2022, pp. 2647-2651.
IEEE DOI 2301
Prototypes, Sensor arrays, Estimation, White noise, Sparse matrices, Signal resolution, Signal processing algorithms, sum co-array BibRef

Zha, Z.Y.[Zhi-Yuan], Wen, B.[Bihan], Yuan, X.[Xin], Ravishankar, S.[Saiprasad], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal sparse and low-rank modeling,
SPMag(40), No. 1, January 2023, pp. 32-44.
IEEE DOI 2301
Deep learning, Computational modeling, Neural networks, Sparse matrices, Imaging, Transforms, Sensors BibRef

Zhang, J.H.[Jun-Hao], Yap, K.H.[Kim-Hui], Chau, L.P.[Lap-Pui], Zhu, C.[Ce],
Nonlocal Low-Rank Residual Modeling for Image Compressive Sensing Reconstruction,
ICIP23(1055-1059)
IEEE DOI 2312
BibRef

Zhou, J.H.[Jian-Hang], Zhang, B.[Bob], Zeng, S.N.[Shao-Ning],
Consensus Sparsity: Multi-Context Sparse Image Representation via L8-Induced Matrix Variate,
IP(32), 2023, pp. 603-616.
IEEE DOI 2301
Image representation, Sparse matrices, Dictionaries, Optimization, Minimization, Image coding, Bayes methods, Sparsity, image analysis BibRef

Eamaz, A.[Arian], Yeganegi, F.[Farhang], Soltanalian, M.[Mojtaba],
On the Building Blocks of Sparsity Measures,
SPLetters(29), 2022, pp. 2667-2671.
IEEE DOI 2301
Energy measurement, Convergence, Particle measurements, Noise measurement, Indexes, Economics, Behavioral sciences, vector norms BibRef

Yu, Q.[Quan], Yang, M.[Ming],
Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics,
PR(137), 2023, pp. 109343.
Elsevier DOI 2302
Tensor completion, Tensor robust principle component analysis, Low-rank approximation, Dynamic background, Spatio-temporal characteristics BibRef

Zhao, J.[Jiaoyan], Liang, Y.S.[Yong-Sheng], Yi, S.[Shuangyan], Shen, Q.Q.[Qiang-Qiang], Cao, X.F.[Xiao-Feng],
Improving generalization of double low-rank representation using Schatten-p norm,
PR(138), 2023, pp. 109352.
Elsevier DOI 2303
Low-rank representation, Schatten- norm, Feature extraction, Subspace clustering BibRef

Peng, J.J.[Jiang-Jun], Wang, Y.[Yao], Zhang, H.Y.[Hong-Ying], Wang, J.J.[Jian-Jun], Meng, D.Y.[De-Yu],
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices,
PAMI(45), No. 5, May 2023, pp. 5766-5781.
IEEE DOI 2304
Sparse matrices, Matrix decomposition, TV, Principal component analysis, Hyperspectral imaging, Histograms, convergence guarantee BibRef

Zou, C.M.[Cui-Ming], Kou, K.I.[Kit Ian], Tang, Y.Y.[Yuan Yan], Deng, H.[Hao],
Adaptive reweighted quaternion sparse learning for data recovery and classification,
PR(142), 2023, pp. 109653.
Elsevier DOI 2307
Quaternion sparse representation, Weight learning, Supervised learning BibRef

Yaseen, H.[Hira], Mahmood, A.[Arif],
Learning Structure Aware Deep Spectral Embedding,
IP(32), 2023, pp. 3939-3948.
IEEE DOI 2307
Clustering algorithms, Manifolds, Laplace equations, Complexity theory, Training, Data structures, Scalability, self-expression learning BibRef

Shi, Z.L.[Zhang-Lei], Li, X.P.[Xiao Peng], Li, W.G.[Wei-Guo], Yan, T.J.[Tong-Jiang], Wang, J.[Jian], Fu, Y.[Yaru],
Robust Low-Rank Matrix Recovery as Mixed Integer Programming via L_0-Norm Optimization,
SPLetters(30), 2023, pp. 1012-1016.
IEEE DOI 2309
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Li, X.P.[Xiao Peng], Yan, Y.[Yi], Kuruoglu, E.E.[Ercan Engin], So, H.C.[Hing Cheung], Chen, Y.[Yuan],
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IEEE DOI 2310
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Fang, P.F.[Peng-Fei], Harandi, M.[Mehrtash], Lan, Z.Z.[Zhen-Zhong], Petersson, L.[Lars],
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IJCV(131), No. 1, January 2023, pp. 2770-2792.
Springer DOI 2310
Embedding data in hyperbolic spaces BibRef

Ji, C.[Cheng], Zhao, T.[Tao], Sun, Q.Y.[Qing-Yun], Fu, X.C.[Xing-Cheng], Li, J.X.[Jian-Xin],
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Elsevier DOI 2310
Higher order, Dynamic network, Heterogeneous network BibRef

Song, Z.H.[Zi-Hao], Xu, X.J.[Xiang-Jian], Lian, H.[Heng], Zhao, W.H.[Wei-Hua],
Robust low tubal rank tensor recovery via L2E criterion,
PR(149), 2024, pp. 110241.
Elsevier DOI 2403
Robust tensor recovery, Tensor factor Frobenius norm, Low tubal rank, criterion, Nonconvex BibRef

Chen, H.R.[Hao-Ran], Chen, X.[Xu], Tao, H.W.[Hong-Wei], Li, Z.[Zuhe], Wang, B.Y.[Bo-Yue],
PDRLRR: A novel low-rank representation with projection distance regularization via manifold optimization for clustering,
PR(149), 2024, pp. 110198.
Elsevier DOI 2403
Low-rank representation, Schatten- norm, Projection distance regularization, Manifold optimization, Data clustering BibRef

Zhang, H.M.[Heng-Min], Wen, B.[Bihan], Zha, Z.Y.[Zhi-Yuan], Zhang, B.[Bob], Tang, Y.[Yang], Yu, G.[Guo], Du, W.L.[Wen-Li],
Accelerated PALM for Nonconvex Low-Rank Matrix Recovery With Theoretical Analysis,
CirSysVideo(34), No. 4, April 2024, pp. 2304-2317.
IEEE DOI Code:
WWW Link. 2404
proximal alternating linearized minimization. Linear matrix inequalities, Minimization, Optimization, Matrix decomposition, Computational modeling, Convergence, low-rank representation BibRef

Wang, J.Y.[Jing-Yu], Deng, T.Q.[Ting-Quan], Yang, M.[Ming],
Nonconvex submodule clustering via joint sliced sparse gradient and cluster-aware approach,
PR(154), 2024, pp. 110619.
Elsevier DOI 2406
Tensor based sparse representation, Submodule clustering, Tensor schatten -norm, Union of free submodules, SSG, Cluster-aware BibRef

Sarkar, R.[Rohan], Kak, A.[Avinash],
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval,
CVPR24(17077-17085)
IEEE DOI 2410
Training, Computer architecture, Object recognition, pose-invariant embeddings, Dual embedding space BibRef

Li, H.[Hanao], Han, T.[Tian],
Enforcing Sparsity on Latent Space for Robust and Explainable Representations,
WACV24(5270-5279)
IEEE DOI 2404
Codes, Sparse approximation, Computational modeling, Semantics, Generators, Robustness, Noise robustness, Algorithms BibRef

An, S.[Sungtae], Jammalamadaka, N.[Nataraj], Chong, E.[Eunji],
Maximum Entropy Information Bottleneck for Uncertainty-aware Stochastic Embedding,
SAIAD23(3809-3818)
IEEE DOI 2309
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Nareddy, K.K.R.[Kartheek K Reddy], Mache, S.[Swapnil], Pokala, P.K.[Praveen Kumar], Seelamantula, C.S.[Chandra Sekhar],
An Ensemble of Proximal Networks for Sparse Coding,
ICIP22(1251-1255)
IEEE DOI 2211
Training, Gradient methods, Image coding, Noise reduction, Image restoration, Iterative methods, Task analysis, image denoising BibRef

Zang, Z.L.[Ze-Lin], Li, S.Y.[Si-Yuan], Wu, D.[Di], Wang, G.[Ge], Wang, K.[Kai], Shang, L.[Lei], Sun, B.[Baigui], Li, H.[Hao], Li, S.Z.[Stan Z.],
DLME: Deep Local-Flatness Manifold Embedding,
ECCV22(XXI:576-592).
Springer DOI 2211
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Usvyatsov, M.[Mikhail], Makarova, A.[Anastasia], Ballester-Ripoll, R.[Rafael], Rakhuba, M.[Maxim], Krause, A.[Andreas], Schindler, K.[Konrad],
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation,
ICCV21(11406-11415)
IEEE DOI 2203
Processes large-scale visual data tensors by looking at a fraction of their entries only. Training, Visualization, Tensors, Medical conditions, Neural networks, Memory management, Optimization and learning methods BibRef

Xu, Z.Q.[Zheng-Qin], Xing, H.S.[Hua-Song], Fang, S.[Shun], Wu, S.Q.[Shi-Qian], Xie, S.L.[Shou-Lie],
Double-Weighted Low-Rank Matrix Recovery Based on Rank Estimation,
RSLCV21(172-180)
IEEE DOI 2112
Adaptation models, Estimation, Sparse matrices, Data mining, Optimization BibRef

Fu, Z.Q.[Zhi-Qiang], Zhao, Y.[Yao], Chang, D.X.[Dong-Xia], Zhang, X.X.[Xing-Xing], Wang, Y.M.[Yi-Ming],
Double low-rank representation with projection distance penalty for clustering,
CVPR21(5316-5325)
IEEE DOI 2111
Laplace equations, Robustness, Complexity theory, Noise measurement, Task analysis BibRef

Botteghi, N.[Nicolò], Obbink, R.[Ruben], Geijs, D.[Daan], Poel, M.[Mannes], Sirmacek, B.[Beril], Brune, C.[Christoph], Mersha, A.[Abeje], Stramigioli, S.[Stefano],
Low Dimensional State Representation Learning with Reward-shaped Priors,
ICPR21(3736-3743)
IEEE DOI 2105

WWW Link. Navigation, Robot vision systems, Reinforcement learning, Robot sensing systems, Trajectory, Topology, Task analysis, Robotics BibRef

Kühnel, L.[Line], Fletcher, T.[Tom], Joshi, S.[Sarang], Sommer, S.[Stefan],
Latent Space Geometric Statistics,
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Springer DOI 2103
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Souza, L.S.[Lincon S.], Sogi, N.[Naoya], Gatto, B.B.[Bernardo B.], Kobayashi, T.[Takumi], Fukui, K.[Kazuhiro],
An Interface between Grassmann manifolds and vector spaces,
Diff-CVML20(3695-3704)
IEEE DOI 2008
Manifolds, Image recognition, Tools, Computational modeling, Kernel, Machine learning, Data models BibRef

Zuo, Y.[Yan], Avraham, G.[Gil], Drummond, T.W.[Tom W.],
Traversing Latent Space Using Decision Ferns,
ACCV18(I:593-608).
Springer DOI 1906
Transforming raw data to a feature space. BibRef

Hu, Z., Nie, F., Li, X.,
Robust Low Rank Approxiamtion via Inliers Selection,
ICIP18(3688-3692)
IEEE DOI 1809
Robustness, Sparse matrices, Approximation algorithms, Data models, Principal component analysis, Structure from motion, Subspace BibRef

Sha, L.D.[Ling-Dao], Schonfeld, D.[Dan],
Dual graph regularized sparse coding for image representation,
VCIP17(1-4)
IEEE DOI 1804
graph theory, image classification, image coding, image representation, image texture, regression analysis, FS-LD, sparse coding BibRef

Kim, J., Tahboub, K., Delp, E.J.,
Spatial pyramid alignment for sparse coding based object classification,
ICIP17(1950-1954)
IEEE DOI 1803
Computational modeling, Encoding, Feature extraction, Image coding, Image representation, Layout, Vector quantization, feature coding, spatial pyramid alignment BibRef

Nascimento, G.[Guilherme], Laranjeira, C.[Camila], Braz, V.[Vinicius], Lacerda, A.[Anisio], Nascimento, E.R.[Erickson R.],
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CIARP17(408-415).
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Chen, C., Zhang, B., del Bue, A.[Alessio], Murino, V.,
Manifold Constrained Low-Rank Decomposition,
RSL-CV17(1800-1808)
IEEE DOI 1802
Data models, Manifolds, Matrix decomposition, Optimization, Transmission line matrix methods, Videos BibRef

Shaheen, S., Affara, L., Ghanem, B.,
Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings,
ICCV17(4434-4442)
IEEE DOI 1802
approximation theory, computer graphics, convergence of numerical methods, convolution, data compression, Standards BibRef

Bibi, A.[Adel], Ghanem, B.[Bernard],
High Order Tensor Formulation for Convolutional Sparse Coding,
ICCV17(1790-1798)
IEEE DOI 1802
To reconstruct color video. image classification, image coding, image colour analysis, image reconstruction, BibRef

Dutta, A.[Aritra], Richtárik, P.[Peter],
Online and Batch Supervised Background Estimation Via L1 Regression,
WACV19(541-550)
IEEE DOI 1904
expectation-maximisation algorithm, gradient methods, learning (artificial intelligence), regression analysis, Streaming media BibRef

Li, X., Dutta, A.[Aritra], Richtárik, P.[Peter],
A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices,
RSL-CV17(1835-1843)
IEEE DOI 1802
Adaptation models, Approximation algorithms, Estimation, Matrix decomposition, Principal component analysis, Robustness, Video sequences BibRef

Li, X., Dutta, A.,
Weighted Low Rank Approximation for Background Estimation Problems,
RSL-CV17(1853-1861)
IEEE DOI 1802
Approximation algorithms, Closed-form solutions, Computational modeling, Estimation, Matrix converters, Robustness BibRef

Dutta, A., Li, X.,
A fast algorithm for a weighted low rank approximation,
MVA17(93-96)
DOI Link 1708
Algorithm design and analysis, Analytical models, Approximation algorithms, Estimation, Matrix decomposition, Principal component analysis, Robustness BibRef

Zhang, L.F.[Li-Fang], Shen, Q.[Qi], Li, D.F.[De-Fang], Tang, X.[Xin], Wang, P.S.[Patrick S.], Feng, G.C.[Guo-Can],
Adaptive Hashing with Sparse Modification,
ICPR16(3844-3849)
IEEE DOI 1705
Binary codes, Distortion, Hypercubes, Linear programming, Principal component analysis, Quantization (signal), Sparse, matrices BibRef

Wang, S.R.[Shu-Run], Zhao, Z.H.[Zheng-Hui], Zhang, X.[Xiang], Zhang, J.[Jian], Wang, S.Q.[Shi-Qi], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Improved entropy of primitive for visual information estimation,
VCIP16(1-4)
IEEE DOI 1701
Convergence BibRef

Qi, N.[Na], Shi, Y.H.[Yun-Hui], Sun, X.Y.[Xiao-Yan], Yin, B.C.[Bao-Cai],
TenSR: Multi-dimensional Tensor Sparse Representation,
CVPR16(5916-5925)
IEEE DOI 1612
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Bernard, F.[Florian], Gemmar, P.[Peter], Hertel, F.[Frank], Goncalves, J.[Jorge], Thunberg, J.[Johan],
Linear Shape Deformation Models with Local Support Using Graph-Based Structured Matrix Factorisation,
CVPR16(5629-5638)
IEEE DOI 1612
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Wei, X.[Xian], Shen, H.[Hao], Kleinsteuber, M.[Martin],
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PAMI(42), No. 12, December 2020, pp. 3119-3135.
IEEE DOI 2011
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Earlier:
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations,
CVPR16(5268-5277)
IEEE DOI 1612
Learning systems, Sparse matrices, Semantics, Machine learning, Machine learning algorithms, Image processing, semi-supervised learning BibRef

Banerjee, M., Chakraborty, R., Vemuri, B.C.,
Sparse Exact PGA on Riemannian Manifolds,
ICCV17(5020-5028)
IEEE DOI 1802
differential geometry, principal component analysis, vectors, PGA problem, Principal Component Analysis, Principal component analysis BibRef

Chakraborty, R.[Rudrasis], Seo, D.H.[Do-Hyung], Vemuri, B.C.[Baba C.],
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds,
CVPR16(3976-3984)
IEEE DOI 1612
A non-linear analog of the PCA algorithm, Principal Geodesic Analysis (PGA). BibRef

Hsieh, S.H.[Sung-Hsien], Lu, C.S.[Chun-Shien], Pei, S.C.[Soo-Chang],
Fast binary embedding via circulant downsampled matrix,
ICIP16(1789-1793)
IEEE DOI 1610
Algorithm design and analysis BibRef

Siyahjani, F.[Farzad], Almohsen, R.[Ranya], Sabri, S.[Sinan], Doretto, G.[Gianfranco],
A Supervised Low-Rank Method for Learning Invariant Subspaces,
ICCV15(4220-4228)
IEEE DOI 1602
Matrix decomposition BibRef

Chum, O.[Ondrej],
Low Dimensional Explicit Feature Maps,
ICCV15(4077-4085)
IEEE DOI 1602
Computer vision BibRef

Sharma, G., Schiele, B.,
Scalable Nonlinear Embeddings for Semantic Category-Based Image Retrieval,
ICCV15(1296-1304)
IEEE DOI 1602
Approximation algorithms BibRef

Wang, X.F.[Xiao-Fei], Navasca, C.[Carmeliza],
Adaptive Low Rank Approximation for Tensors,
RSL-CV15(939-945)
IEEE DOI 1602
Adaptation models BibRef

Bian, X.[Xiao], Krim, H.[Hamid],
Bi-sparsity pursuit for robust subspace recovery,
ICIP15(3535-3539)
IEEE DOI 1512
Sparse representation BibRef

Provenzi, E.[Edoardo], Delon, J.[Julie], Gousseau, Y.[Yann], Mazin, B.[Baptiste],
On Spatiochromatic Features in Natural Images Statistics,
CIAP15(II:46-55).
Springer DOI 1511
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Xiao, S.J.[Shi-Jie], Li, W.[Wen], Xu, D.[Dong], Tao, D.C.[Da-Cheng],
FaLRR: A fast low rank representation solver,
CVPR15(4612-4620)
IEEE DOI 1510
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Heide, F.[Felix], Heidrich, W.[Wolfgang], Wetzstein, G.[Gordon],
Fast and flexible convolutional sparse coding,
CVPR15(5135-5143)
IEEE DOI 1510
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Zhang, X.[Xin], Phung, D.Q.[Dinh Q.], Venkatesh, S.[Svetha], Pham, D.S.[Duc-Son], Liu, W.Q.[Wan-Quan],
Multi-View Subspace Clustering for Face Images,
DICTA15(1-7)
IEEE DOI 1603
computer vision BibRef

Zhang, X.[Xin], Pham, D.S.[Duc-Son], Phung, D.Q.[Dinh Q.], Liu, W.Q.[Wan-Quan], Saha, B.[Budhaditya], Venkatesh, S.[Svetha],
Visual Object Clustering via Mixed-Norm Regularization,
WACV15(1030-1037)
IEEE DOI 1503
Clustering algorithms BibRef

Zhang, H.[Heng], Patel, V.M.[Vishal M.], Shekhar, S.[Sumit], Chellappa, R.[Rama],
Domain adaptive sparse representation-based classification,
FG15(1-8)
IEEE DOI 1508
biometrics (access control) BibRef

Shekhar, S.[Sumit], Patel, V.M.[Vishal M.], Chellappa, R.[Rama],
Analysis sparse coding models for image-based classification,
ICIP14(5207-5211)
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ICIP14(2864-2868)
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Cost function BibRef

Liu, G.W.[Gao-Wen], Yan, Y.[Yan], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu],
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ICIP14(2869-2873)
IEEE DOI 1502
Accuracy BibRef

Lin, T.Y.[Tsung-Yu], Liu, T.L.[Tyng-Luh],
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ICIP14(2212-2216)
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Wang, Y.Q.[Yu-Qi], Gong, Y.F.[Yun-Fei], Liu, Q.A.[Qi-Ang],
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ICPR14(3744-3749)
IEEE DOI 1412
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Xing, S.[Sun], Yung, N.H.C.[Nelson H.C.],
Large Scale Image Categorization in Sparse Nonparametric Bayesian Representation,
ICPR14(1365-1370)
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Luo, L.[Lei], Yang, J.[Jian], Qian, J.J.[Jian-Jun], Yang, J.Y.[Jing-Yu],
Nuclear Norm Regularized Sparse Coding,
ICPR14(1834-1839)
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Databases BibRef

Li, W.[Wanyi], Wang, P.[Peng], Qiao, H.[Hong],
Visual Tracking via Saliency Weighted Sparse Coding Appearance Model,
ICPR14(4092-4097)
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Choi, J.H.[Jong-Hyun], Cho, H.J.[Hyun-Jong], Kwac, J.[Jungsuk], Davis, L.S.[Larry S.],
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ICPR14(4423-4428)
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Tao, L.[Liang], Ip, H.H.S.[Horace H.S.], Wang, Y.L.[Ying-Lin], Shu, X.[Xin],
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Li, J.X.[Jun-Xia], Rajan, D.[Deepu], Yang, J.[Jian],
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ICIP15(1300-1304)
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estimation theory BibRef

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CVPR14(2713-2720)
IEEE DOI 1409
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Mobahi, H.[Hossein], Liu, C.[Ce], Freeman, W.T.[William T.],
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Izadinia, H.[Hamid], Sadeghi, F.[Fereshteh], Farhadi, A.[Ali],
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CVPR14(232-239)
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data assimilation BibRef

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ICIP13(3013-3016)
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Zhang, H.[Hong], Chen, L.[Li],
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ICIP13(3959-3962)
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correlation analysis BibRef

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ICIP13(335-339)
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Bo, L.F.[Lie-Feng], Ren, X.F.[Xiao-Feng], Fox, D.[Dieter],
Multipath Sparse Coding Using Hierarchical Matching Pursuit,
CVPR13(660-667)
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Deep Learning; Feature Learning; Object Recognition; Sparse Coding BibRef

Bristow, H.[Hilton], Eriksson, A.P.[Anders P.], Lucey, S.[Simon],
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CVPR13(391-398)
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ADMM; convolution; deep learning; fourier; sparse coding BibRef

Chi, Y.T.[Yu-Tseh], Ali, M.[Mohsen], Rushdi, M.[Muhammad], Ho, J.[Jeffrey],
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Sparse coding; affine; classification; group BibRef

Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming], Yin, B.C.[Bao-Cai], Qin, L.[Lei], Wang, D.[Dan],
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ICIP13(3274-3277)
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Wang, Y.M.[Yue-Ming], Wang, X.G.[Xing-Gang], Zhu, S.J.[Shao-Jun], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu],
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Zheng, P., Aravkin, A.Y., Thiagarajan, J.J.[Jayaraman J.], Ramamurthy, K.N.,
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IEEE DOI 1802
Dynamics, Optimization, Principal component analysis, Robustness, TV BibRef

Thiagarajan, J.J.[Jayaraman J.], Ramamurthy, K.N.[Karthikeyan Natesan], Sattigeri, P.[Prasanna], Spanias, A.[Andreas],
Supervised local sparse coding of sub-image features for image retrieval,
ICIP12(3117-3120).
IEEE DOI 1302
BibRef

Zhang, L.[Lihe], Ma, C.[Chen],
Low-rank, sparse matrix decomposition and group sparse coding for image classification,
ICIP12(669-672).
IEEE DOI 1302
BibRef

Raja, R., Mansoor Roomi, S.M., Dharmalakshmi, D.,
Robust indoor/outdoor scene classification,
ICAPR15(1-5)
IEEE DOI 1511
BibRef
Earlier:
Outdoor scene classification using invariant features,
NCVPRIPG13(1-4)
IEEE DOI 1408
Gabor filters. feature extraction BibRef

Sathyabama, B., Mansoor Roomi, S.M., Kamalam R, E.J.,
Geometric invariant Target classification using 2D Mellin cepstrum with modified grid formation,
NCVPRIPG13(1-4)
IEEE DOI 1408
Fourier transforms BibRef

Raja, R., Mansoor Roomi, S.M., Kalaiyarasi, D.,
Semantic modeling of natural scenes by local binary pattern,
IMVIP12(169-172).
IEEE DOI 1302
BibRef

Julazadeh, A.[Ali], Marsousi, M.[Mahdi], Alirezaie, J.[Javad],
Classification based on sparse representation and Euclidian distance,
VCIP12(1-5).
IEEE DOI 1302
BibRef

Zeng, Z.[Zinan], Chan, T.H.[Tsung-Han], Jia, K.[Kui], Xu, D.[Dong],
Finding Correspondence from Multiple Images via Sparse and Low-Rank Decomposition,
ECCV12(V: 325-339).
Springer DOI 1210
BibRef

Jiang, Z.L.[Zhuo-Lin], Davis, L.S.[Larry S.],
Submodular Salient Region Detection,
CVPR13(2043-2050)
IEEE DOI 1309
BibRef

Cao, L.J.[Liu-Juan], Ji, R.R.[Rong-Rong], Gao, Y.[Yue], Yang, Y.[Yi], Tian, Q.[Qi],
Weakly supervised sparse coding with geometric consistency pooling,
CVPR12(3578-3585).
IEEE DOI 1208
BibRef

Liu, H.F.[Hai-Feng], Yang, Z.[Zheng], Wu, Z.H.[Zhao-Hui], Li, X.L.[Xue-Long],
A-Optimal Non-negative Projection for image representation,
CVPR12(1592-1599).
IEEE DOI 1208
BibRef

Liu, R.S.[Ri-Sheng], Lin, Z.C.[Zhou-Chen], de la Torre, F.[Fernando], Su, Z.X.[Zhi-Xun],
Fixed-rank representation for unsupervised visual learning,
CVPR12(598-605).
IEEE DOI 1208
BibRef

Liu, L.Q.[Ling-Qiao], Wang, L.[Lei], Liu, X.W.[Xin-Wang],
In defense of soft-assignment coding,
ICCV11(2486-2493).
IEEE DOI 1201
Computationally efficient, but not as accurate as sparse or local coding (which is computationally more expensive) BibRef

Sohn, K.[Kihyuk], Jung, D.Y.[Dae Yon], Lee, H.L.[Hong-Lak], Hero, A.O.[Alfred O.],
Efficient learning of sparse, distributed, convolutional feature representations for object recognition,
ICCV11(2643-2650).
IEEE DOI 1201
BibRef

Robles-Kelly, A.[Antonio],
Learning a Gaussian basis for spectra representation aimed at reflectance classification,
OTCBVS11(88-95).
IEEE DOI 1106
BibRef

Zontak, M.[Maria], Irani, M.[Michal],
Internal statistics of a single natural image,
CVPR11(977-984).
IEEE DOI 1106
From recurrence of small image patches. Priors for solving problems. BibRef

Cai, D.[Deng], Bao, H.J.[Hu-Jun], He, X.F.[Xiao-Fei],
Sparse concept coding for visual analysis,
CVPR11(2905-2910).
IEEE DOI 1106
Sparse Concept Coding, to capture geometric structure more than SVD does. BibRef

He, R.[Ran], Zheng, W.S.[Wei-Shi], Hu, B.G.[Bao-Gang], Kong, X.W.[Xiang-Wei],
Nonnegative sparse coding for discriminative semi-supervised learning,
CVPR11(2849-2856).
IEEE DOI 1106
BibRef

Kulkarni, N.[Naveen], Li, B.X.[Bao-Xin],
Discriminative affine sparse codes for image classification,
CVPR11(1609-1616).
IEEE DOI 1106
BibRef

Yu, K.[Kai], Lin, Y.Q.[Yuan-Qing], Lafferty, J.[John],
Learning image representations from the pixel level via hierarchical sparse coding,
CVPR11(1713-1720).
IEEE DOI 1106
BibRef

Bespalov, D.[Dmitriy], Dahl, A.L.[Anders Lindbjerg], Bai, B.[Bing], Shokoufandeh, A.[Ali],
On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding,
SCIA11(103-113).
Springer DOI 1105
BibRef

Wu, L.[Lina], Luo, S.W.[Si-Wei], Sun, W.[Wei], Zheng, X.[Xiang],
Integrating ILSR to Bag-of-Visual Words Model Based on Sparse Codes of SIFT Features Representations,
ICPR10(4283-4286).
IEEE DOI 1008
Implicit local spatial relationship. ILSR Sparse codes of SIFT features. BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Ruan, X.[Xiang],
Image recognition by learned linear subspace of combined bag-of-features and low-level features,
ICIP10(1049-1052).
IEEE DOI 1009
BibRef
And:
Image Categorization by Learned Nonlinear Subspace of Combined Visual-Words and Low-Level Features,
ICPR10(3037-3040).
IEEE DOI 1008
Object and scene classes. BibRef

Zhan, Y.B.[Yu-Bin], Yin, J.P.[Jian-Ping],
Cluster Preserving Embedding,
ICPR10(621-624).
IEEE DOI 1008
BibRef

Przelaskowski, A.[Artur],
The Role of Sparse Data Representation in Semantic Image Understanding,
ICCVG10(I: 69-80).
Springer DOI 1009
BibRef

Huang, J.B.[Jia-Bin], Yang, M.H.[Ming-Hsuan],
Fast sparse representation with prototypes,
CVPR10(3618-3625).
IEEE DOI 1006
BibRef

Liu, Y.[Yanan], Wu, F.[Fei], Zhang, Z.H.[Zhi-Hua], Zhuang, Y.T.[Yue-Ting], Yan, S.C.[Shui-Cheng],
Sparse representation using nonnegative curds and whey,
CVPR10(3578-3585).
IEEE DOI 1006
Set of sparse and nonnegative representations. Then incorporate these into a sparse representation. BibRef

Gong, D.[Dian], Zhao, X.M.[Xue-Mei], Yang, Q.[Qiong],
Sparse Non-negative Pattern Learning for image representation,
ICIP08(981-984).
IEEE DOI 0810
Patterns are learned, features are extracted then used for representation. BibRef

Tsai, Y.T.[Yun-Ta], Wang, Q.[Quan], You, S.[Suya],
CDIKP: A highly-compact local feature descriptor,
ICPR08(1-4).
IEEE DOI 0812
SIFT combined with projection BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Controlling Sparseness in Non-negative Tensor Factorization,
ECCV06(I: 56-67).
Springer DOI 0608
BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Reverse-Convex Programming for Sparse Image Codes,
EMMCVPR05(600-616).
Springer DOI 0601
BibRef
And:
Learning Non-Negative Sparse Image Codes by Convex Programming,
ICCV05(II: 1667-1674).
IEEE DOI 0510
Aim to preserve local structure, unlike PCA.
See also Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. BibRef

Polak, S.[Simon], Shashua, A.[Amnon],
The Semi-explicit Shape Model for Multi-object Detection and Classification,
ECCV10(II: 336-349).
Springer DOI 1009
BibRef

Shashua, A.[Amnon], Zass, R.[Ron], Hazan, T.[Tamir],
Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization,
ECCV06(IV: 595-608).
Springer DOI 0608

See also Probabilistic graph and hypergraph matching. BibRef

Hazan, T.[Tamir], Polak, S.[Simon], Shashua, A.[Amnon],
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization,
ICCV05(I: 50-57).
IEEE DOI 0510
Generate descriptions (e.g. bases) of images. BibRef

Haasdonk, B., Halawani, A., Burkhardt, H.,
Adjustable Invariant Features by Partial Haar-Integration,
ICPR04(II: 769-774).
IEEE DOI 0409
BibRef

Molina-Gamez, M., Subirana-Vilanova, J.B.,
Sparse Groups: A Polynomial Middle-Level Approach for Object Recognition,
ICPR96(I: 518-522).
IEEE DOI 9608
(Autonomous Univ. of Barcelona, E) BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Sparse Descriptions, Dictionary Descriptions .