14.2.6.1 Subspace Clustering

Chapter Contents (Back)
Subspace Clustering.

Gan, G., Wu, J.,
A convergence theorem for the fuzzy subspace clustering (FSC) algorithm,
PR(41), No. 6, June 2008, pp. 1939-1947.
Elsevier DOI 0802
Clustering; Subspace clustering; Analysis of algorithms; Convergence; Fuzzy set BibRef

Deng, Z.H.[Zhao-Hong], Choi, K.S.[Kup-Sze], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
Enhanced soft subspace clustering integrating within-cluster and between-cluster information,
PR(43), No. 3, March 2010, pp. 767-781.
Elsevier DOI 1001
Subspace clustering; Soft subspace; Weighted clustering; Gene expression clustering analysis; Texture image segmentation; [epsilon]-insensitive distance Comment: See also Comment on 'Enhanced soft subspace clustering integrating within-cluster and between-cluster information' by Z. Deng et al. (Pattern Recognition, vol. 43, pp. 767-781, 2010). BibRef

Bai, L.[Liang], Liang, J.[Jiye], Dang, C.Y.[Chuang-Yin], Cao, F.Y.[Fu-Yuan],
A novel attribute weighting algorithm for clustering high-dimensional categorical data,
PR(44), No. 12, December 2011, pp. 2843-2861.
Elsevier DOI 1107
Cluster analysis; Optimization algorithm; High-dimensional categorical data; Subspace clustering; Attribute weighting BibRef

Peng, L.Q.[Liu-Qing], Zhang, J.Y.[Jun-Ying],
An entropy weighting mixture model for subspace clustering of high-dimensional data,
PRL(32), No. 8, 1 June 2011, pp. 1154-1161.
Elsevier DOI 1101
Subspace clustering; High-dimensional data; Gaussian mixture models; Local feature relevance; Shape volume BibRef

Ahmad, A.[Amir], Dey, L.[Lipika],
A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets,
PRL(32), No. 7, 1 May 2011, pp. 1062-1069.
Elsevier DOI 1101
Clustering; Subspace clustering; Mixed data; Categorical data BibRef

Chen, X.J.[Xiao-Jun], Ye, Y.M.[Yun-Ming], Xu, X.F.[Xiao-Fei], Huang, J.Z.[Joshua Zhexue],
A feature group weighting method for subspace clustering of high-dimensional data,
PR(45), No. 1, 2012, pp. 434-446.
Elsevier DOI 1410
Data mining BibRef

Jing, L.P.[Li-Ping], Tian, K.[Kuang], Huang, J.Z.[Joshua Z.],
Stratified feature sampling method for ensemble clustering of high dimensional data,
PR(48), No. 11, 2015, pp. 3688-3702.
Elsevier DOI 1506
Stratified sampling BibRef

Ng, T.F.[Theam Foo], Pham, T.D.[Tuan D.], Jia, X.P.[Xiu-Ping],
Feature interaction in subspace clustering using the Choquet integral,
PR(45), No. 7, July 2012, pp. 2645-2660.
Elsevier DOI 1203
Subspace clustering; Fuzzy clustering; Choquet integral; Fuzzy measure; Feature interaction; Pattern recognition BibRef

Pham, T.D.[Tuan D.], Brandl, M.[Miriam], Beck, D.[Dominik],
Fuzzy declustering-based vector quantization,
PR(42), No. 11, November 2009, pp. 2570-2577.
Elsevier DOI 0907
Vector quantization; Declustering; Fuzzy c-means; Fuzzy partition entropy; Distortion measures; Pattern classification See also Fuzzy posterior-probabilistic fusion. BibRef

Ng, T.F.[Theam Foo], Pham, T.D.[Tuan D.], Sun, C.M.[Chang-Ming],
Automated Feature Weighting in Fuzzy Declustering-based Vector Quantization,
ICPR10(686-689).
IEEE DOI 1008
BibRef

Xia, H.[Hu], Zhuang, J.[Jian], Yu, D.H.[De-Hong],
Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data,
PR(46), No. 9, September 2013, pp. 2562-2575.
Elsevier DOI 1305
Subspace clustering; Multi-objective evolutionary algorithm; Determination of the best solution; Determination of the cluster number BibRef

Adler, A., Elad, M.[Michael], Hel-Or, Y.[Yacov],
Probabilistic Subspace Clustering Via Sparse Representations,
SPLetters(20), No. 1, January 2013, pp. 63-66.
IEEE DOI 1212
BibRef

Liu, J.M.[Jun-Min], Chen, Y.J.[Yi-Jun], Zhang, J.S.[Jiang-She], Xu, Z.B.[Zong-Ben],
Enhancing Low-Rank Subspace Clustering by Manifold Regularization,
IP(23), No. 9, September 2014, pp. 4022-4030.
IEEE DOI 1410
data structures BibRef

Gan, G.J.[Guo-Jun], Ng, M.K.P.[Michael Kwok-Po],
Subspace clustering using affinity propagation,
PR(48), No. 4, 2015, pp. 1455-1464.
Elsevier DOI 1502
Data clustering BibRef

Gan, G.J.[Guo-Jun], Ng, M.K.P.[Michael Kwok-Po],
Subspace clustering with automatic feature grouping,
PR(48), No. 11, 2015, pp. 3703-3713.
Elsevier DOI 1506
Data clustering BibRef

Gan, G.J.[Guo-Jun], Ng, M.K.P.[Michael Kwok-Po],
k-means clustering with outlier removal,
PRL(90), No. 1, 2017, pp. 8-14.
Elsevier DOI 1704
Data clustering BibRef

Zhao, X.Y.[Xue-Yi], Zhang, C.Y.[Chen-Yi], Zhang, Z.F.[Zhong-Fei],
Distributed cross-media multiple binary subspace learning,
MultInfoRetr(4), No. 2, June 2015, pp. 153-164.
Springer DOI 1506
BibRef

Hu, H., Feng, J., Zhou, J.,
Exploiting Unsupervised and Supervised Constraints for Subspace Clustering,
PAMI(37), No. 8, August 2015, pp. 1542-1557.
IEEE DOI 1507
Cameras BibRef

Xu, J.[Jun], Xu, K.[Kui], Chen, K.[Ke], Ruan, J.[Jishou],
Reweighted sparse subspace clustering,
CVIU(138), No. 1, 2015, pp. 25-37.
Elsevier DOI 1506
Subspace clustering BibRef

Kang, Z.[Zhao], Peng, C.[Chong], Cheng, Q.A.[Qi-Ang],
Robust Subspace Clustering via Smoothed Rank Approximation,
SPLetters(22), No. 11, November 2015, pp. 2088-2092.
IEEE DOI 1509
approximation theory BibRef

Peng, C.[Chong], Kang, Z.[Zhao], Cheng, Q.A.[Qi-Ang],
Subspace Clustering via Variance Regularized Ridge Regression,
CVPR17(682-691)
IEEE DOI 1711
Clustering methods, Data models, Manifolds, Mathematical model, Optimization, Tensile stress. BibRef

Peng, C.[Chong], Kang, Z.[Zhao], Yang, M., Cheng, Q.A.[Qi-Ang],
Feature Selection Embedded Subspace Clustering,
SPLetters(23), No. 7, July 2016, pp. 1018-1022.
IEEE DOI 1608
convex programming BibRef

Wang, Y.[Yang], Lin, X.M.[Xue-Min], Wu, L.[Lin], Zhang, W.J.[Wen-Jie], Zhang, Q.[Qing], Huang, X.D.[Xiao-Di],
Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus,
IP(24), No. 11, November 2015, pp. 3939-3949.
IEEE DOI 1509
compressed sensing BibRef

Yin, M.[Ming], Gao, J.B.[Jun-Bin], Lin, Z.C.[Zhou-Chen], Shi, Q.F.[Qin-Feng], Guo, Y.[Yi],
Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering,
IP(24), No. 12, December 2015, pp. 4918-4933.
IEEE DOI 1512
computational geometry BibRef

Yin, M.[Ming], Xie, S.L.[Sheng-Li], Wu, Z.Z.[Zong-Ze], Zhang, Y.[Yun], Gao, J.B.[Jun-Bin],
Subspace Clustering via Learning an Adaptive Low-Rank Graph,
IP(27), No. 8, August 2018, pp. 3716-3728.
IEEE DOI 1806
computer vision, graph theory, image representation, learning (artificial intelligence), matrix algebra, subspace clustering BibRef

Yin, M.[Ming], Gao, J.B.[Jun-Bin], Lin, Z.C.[Zhou-Chen],
Laplacian Regularized Low-Rank Representation and Its Applications,
PAMI(38), No. 3, March 2016, pp. 504-517.
IEEE DOI 1602
Data models BibRef

He, R.[Ran], Zhang, M.[Man], Wang, L.[Liang], Ji, Y.[Ye], Yin, Q.[Qiyue],
Cross-Modal Subspace Learning via Pairwise Constraints,
IP(24), No. 12, December 2015, pp. 5543-5556.
IEEE DOI 1512
Internet BibRef

Babaeian, A.[Amir], Babaee, M.[Mohammadreaza], Bayestehtashk, A.[Alireza], Bandarabadi, M.[Mojtaba],
Nonlinear subspace clustering using curvature constrained distances,
PRL(68, Part 1), No. 1, 2015, pp. 118-125.
Elsevier DOI 1512
Subspace clustering BibRef

Chen, L.[Lifei], Wang, S.[Shengrui], Wang, K.[Kaijun], Zhu, J.P.[Jian-Ping],
Soft subspace clustering of categorical data with probabilistic distance,
PR(51), No. 1, 2016, pp. 322-332.
Elsevier DOI 1601
Subspace clustering BibRef

Luo, C., Ni, B., Yan, S., Wang, M.,
Image Classification by Selective Regularized Subspace Learning,
MultMed(18), No. 1, January 2016, pp. 40-50.
IEEE DOI 1601
Encoding BibRef

Su, S.Z.[Shu-Zhi], Ge, H.W.[Hong-Wei], Yuan, Y.H.[Yun-Hao],
Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning,
JVCIR(36), No. 1, 2016, pp. 69-79.
Elsevier DOI 1603
Kernel matrix optimization BibRef

Washizawa, Y.[Yoshikazu],
Learning Subspace Classification Using Subset Approximated Kernel Principal Component Analysis,
IEICE(E99-D), No. 5, May 2016, pp. 1353-1363.
WWW Link. 1605
BibRef

Fang, X.Z.[Xiao-Zhao], Xu, Y.[Yong], Li, X., Lai, Z., Wong, W.K.,
Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation,
Cyber(46), No. 8, August 2016, pp. 1828-1838.
IEEE DOI 1608
Clustering algorithms BibRef

Fei, L.[Lunke], Xu, Y.[Yong], Fang, X.Z.[Xiao-Zhao], Yang, J.[Jian],
Low rank representation with adaptive distance penalty for semi-supervised subspace classification,
PR(67), No. 1, 2017, pp. 252-262.
Elsevier DOI 1704
Low rank representation BibRef

Li, Q.[Qi], Sun, Z.A.[Zhen-An], Lin, Z.C.[Zhou-Chen], He, R.[Ran], Tan, T.N.[Tie-Niu],
Transformation invariant subspace clustering,
PR(59), No. 1, 2016, pp. 142-155.
Elsevier DOI 1609
Transformation BibRef

Yuan, M.D.[Ming-Dong], Feng, D.Z.[Da-Zheng], Liu, W.J.[Wen-Juan], Xiao, C.B.[Chun-Bao],
Collaborative representation discriminant embedding for image classification,
JVCIR(41), No. 1, 2016, pp. 212-224.
Elsevier DOI 1612
Subspace learning BibRef

Yin, Q.[Qiyue], Wu, S.[Shu], Wang, L.[Liang],
Unified subspace learning for incomplete and unlabeled multi-view data,
PR(67), No. 1, 2017, pp. 313-327.
Elsevier DOI 1704
Multi-view learning BibRef

Ma, C., Tsang, I.W., Peng, F., Liu, C.,
Partial Hash Update via Hamming Subspace Learning,
IP(26), No. 4, April 2017, pp. 1939-1951.
IEEE DOI 1704
Binary codes BibRef

Shi, D.[Daming], Wang, J.[Jun], Cheng, D.[Dansong], Gao, J.[Junbin],
A global-local affinity matrix model via EigenGap for graph-based subspace clustering,
PRL(89), No. 1, 2017, pp. 67-72.
Elsevier DOI 1704
Spectral clustering BibRef

Wang, Z.Y.[Zi-Yu], Xue, J.H.[Jing-Hao],
The matched subspace detector with interaction effects,
PR(68), No. 1, 2017, pp. 24-37.
Elsevier DOI 1704
Matched subspace detector (MSD) BibRef

Peng, C., Kang, Z., Xu, F., Chen, Y., Cheng, Q.,
Image Projection Ridge Regression for Subspace Clustering,
SPLetters(24), No. 7, July 2017, pp. 991-995.
IEEE DOI 1706
image enhancement, image representation, pattern clustering, 2D data, image enhancement, image projection ridge regression, image representation, subspace clustering method, two-dimensional data, vector, Clustering methods, Convergence, Covariance matrices, Face, Linear programming, Optimization, 2-diemnsional data, Subspace clustering, spatial information, unsupervised, learning BibRef

Brbic, M.[Maria], Kopriva, I.[Ivica],
Multi-view low-rank sparse subspace clustering,
PR(73), No. 1, 2018, pp. 247-258.
Elsevier DOI 1709
Subspace clustering BibRef

Yan, Q.[Qing], Ding, Y.[Yun], Xia, Y.[Yi], Chong, Y.W.[Yan-Wen], Zheng, C.H.[Chun-Hou],
Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Ding, Y.[Yun], Pan, S.M.[Shao-Ming], Chong, Y.W.[Yan-Wen],
Robust Spatial-Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification,
GeoRS(58), No. 3, March 2020, pp. 1747-1762.
IEEE DOI 2003
Hyperspectral imaging, Semantics, Manifolds, Image restoration, Probabilistic logic, Block-diagonal representation, typicalness BibRef

Li, B.[Bo], Liu, R.S.[Ri-Sheng], Cao, J.J.[Jun-Jie], Zhang, J.[Jie], Lai, Y.K.[Yu-Kun], Liu, X.P.[Xiu-Ping],
Online Low-Rank Representation Learning for Joint Multi-Subspace Recovery and Clustering,
IP(27), No. 1, January 2018, pp. 335-348.
IEEE DOI 1712
computational complexity, image representation, learning (artificial intelligence), pattern clustering, subspace learning BibRef

Rahmani, M., Atia, G.K.,
Subspace Clustering via Optimal Direction Search,
SPLetters(24), No. 12, December 2017, pp. 1793-1797.
IEEE DOI 1712
convex programming, image segmentation, pattern clustering, search problems, unsupervised learning BibRef

Liu, H.J.[Hai-Jun], Cheng, J.[Jian], Wang, F.[Feng],
Sequential Subspace Clustering via Temporal Smoothness for Sequential Data Segmentation,
IP(27), No. 2, February 2018, pp. 866-878.
IEEE DOI 1712
BibRef
Earlier:
Sequential Subspace Clustering via Temporal Smoothness,
FG17(245-250)
IEEE DOI 1707
Clustering algorithms, Clustering methods, Data models, Dictionaries, Encoding, Image coding, Optimization, temporal smoothness. Face, Linear programming. BibRef

Tan, H.L.[Heng-Liang], Gao, Y.[Ying], Ma, Z.M.[Zheng-Ming],
Regularized constraint subspace based method for image set classification,
PR(76), No. 1, 2018, pp. 434-448.
Elsevier DOI 1801
Subspace method BibRef

Forghani, Y.[Yahya],
Comment on 'Enhanced soft subspace clustering integrating within-cluster and between-cluster information' by Z. Deng et al. (Pattern Recognition, vol. 43, pp. 767-781, 2010),
PR(77), 2018, pp. 456-457.
Elsevier DOI 1802
Fuzzy c-means, Enhanced soft subspace clustering, Convex, Concave See also Enhanced soft subspace clustering integrating within-cluster and between-cluster information. BibRef

Chen, Y., Li, G., Gu, Y.,
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering,
SPLetters(25), No. 2, February 2018, pp. 164-168.
IEEE DOI 1802
computational complexity, greedy algorithms, optimisation, pattern clustering, SSC algorithms, active OMP-SSC, subspace detection property (SDP) BibRef

Zhang, J., Li, X., Jing, P., Liu, J., Su, Y.,
Low-Rank Regularized Heterogeneous Tensor Decomposition for Subspace Clustering,
SPLetters(25), No. 3, March 2018, pp. 333-337.
IEEE DOI 1802
Algorithm design and analysis, Clustering algorithms, Matrix decomposition, Robustness, Signal processing algorithms, tensor decomposition BibRef

Li, B., Lu, H., Li, F., Wu, W.,
Subspace Clustering With K-Support Norm,
CirSysVideo(28), No. 2, February 2018, pp. 302-313.
IEEE DOI 1802
Clustering algorithms, Clustering methods, Correlation, Data models, Databases, Optimization, Sparse matrices, grouping effect BibRef

Diaz-Chito, K.[Katerine], del Rincón, J.M.[Jesús Martínez], Hernández-Sabaté, A.[Aura], Rusiñol, M.[Marçal], Ferri, F.J.[Francesc J.],
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction,
JMIV(60), No. 4, May 2018, pp. 512-524.
Springer DOI 1804
Supervised subspace learning. BibRef

Diaz-Chito, K.[Katerine], del Rincón, J.M.[Jesús Martínez], Rusiñol, M.[Marçal], Hernández-Sabaté, A.[Aura],
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors,
JMIV(61), No. 3, March 2019, pp. 331-351.
Springer DOI 1903
BibRef

Zhu, W.C.[Wen-Cheng], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Nonlinear subspace clustering for image clustering,
PRL(107), 2018, pp. 131-136.
Elsevier DOI 1805
BibRef
Earlier:
Nonlinear subspace clustering,
ICIP17(4497-4501)
IEEE DOI 1803
Subspace clustering, Neural network, Nonlinear transformation, Local similarity. Clustering algorithms, Clustering methods, Face, Laplace equations, Neural networks, Optimization, Principal component analysis, nonlinear transformation BibRef

Zhu, W.C.[Wen-Cheng], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Structured general and specific multi-view subspace clustering,
PR(93), 2019, pp. 392-403.
Elsevier DOI 1906
Subspace clustering, Multi-view learning, Structure consistence, Diversity BibRef

Tolic, D.[Dijana], Antulov-Fantulin, N.[Nino], Kopriva, I.[Ivica],
A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering,
PR(82), 2018, pp. 40-55.
Elsevier DOI 1806
Subspace clustering, Non-negative matrix factorization, Orthogonality, Kernels, Graph regularization BibRef

Chen, H.Z.[Hua-Zhu], Wang, W.W.[Wei-Wei], Feng, X.C.[Xiang-Chu],
Structured Sparse Subspace Clustering with Within-Cluster Grouping,
PR(83), 2018, pp. 107-118.
Elsevier DOI 1808
Subspace clustering, Grouping-effect-within-clusters, Affinity matrix learning BibRef

Wang, W.W.[Wei-Wei], Yang, C.Y.[Chun-Yu], Chen, H.Z.[Hua-Zhu], Feng, X.C.[Xiang-Chu],
Unified Discriminative and Coherent Semi-Supervised Subspace Clustering,
IP(27), No. 5, May 2018, pp. 2461-2470.
IEEE DOI 1804
Clustering methods, Coherence, Computer vision, Harmonic analysis, Manifolds, Optimization, Sparse matrices, Discrimination, coherence, subspace clustering BibRef

Zhu, Y.[Ye], Ting, K.M.[Kai Ming], Carman, M.J.[Mark J.],
Grouping points by shared subspaces for effective subspace clustering,
PR(83), 2018, pp. 230-244.
Elsevier DOI 1808
Subspace clustering, Shared subspaces, Density-based clustering BibRef

Peng, X., Feng, J., Xiao, S., Yau, W., Zhou, J.T., Yang, S.,
Structured AutoEncoders for Subspace Clustering,
IP(27), No. 10, October 2018, pp. 5076-5086.
IEEE DOI 1808
data handling, learning (artificial intelligence), neural nets, pattern clustering, StructAE, prior structured information, spectral clustering BibRef

Pesevski, A.[Angelina], Franczak, B.C.[Brian C.], McNicholas, P.D.[Paul D.],
Subspace clustering with the multivariate-t distribution,
PRL(112), 2018, pp. 297-302.
Elsevier DOI 1809
Finite mixture models, Multivariate- distribution, EM algorithm, Dimension reduction, Subspace clustering BibRef

Xia, Y.Q.[Yu-Qing], Zhang, Z.Y.[Zhen-YueA],
Rank-sparsity balanced representation for subspace clustering,
RealTimeIP(14), No. 1, January 2018, pp. 979-990.
WWW Link. 1809
BibRef

Yang, Y.Z.[Ying-Zhen], Feng, J.[Jiashi], Jojic, N.[Nebojsa], Yang, J.C.[Jian-Chao], Huang, T.S.[Thomas S.],
Subspace Learning by L0-Induced Sparsity,
IJCV(126), No. 10, October 2018, pp. 1138-1156.
Springer DOI 1809
BibRef

Xie, Y.[Yuan], Tao, D.C.[Da-Cheng], Zhang, W.S.[Wen-Sheng], Liu, Y.[Yan], Zhang, L.[Lei], Qu, Y.[Yanyun],
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization,
IJCV(126), No. 11, November 2018, pp. 1157-1179.
Springer DOI 1809
multi-view subspace clustering problem BibRef

Zhu, Q.H.[Qi-Hai], Yang, Y.B.[Yu-Bin],
Subspace clustering via seeking neighbors with minimum reconstruction error,
PRL(115), 2018, pp. 66-73.
Elsevier DOI 1812
Subsapce clustering, Sparse representation, Minimum reconstruction error, Dictionary learning BibRef

Lu, C.Y.[Can-Yi], Feng, J.S.[Jia-Shi], Lin, Z.C.[Zhou-Chen], Mei, T.[Tao], Yan, S.C.[Shui-Cheng],
Subspace Clustering by Block Diagonal Representation,
PAMI(41), No. 2, February 2019, pp. 487-501.
IEEE DOI 1901
Clustering methods, Symmetric matrices, Computer vision, Minimization, Convergence, Optimization, Subspace clustering, convergence analysis BibRef

Wang, X.B.[Xiao-Bo], Lei, Z.[Zhen], Guo, X.[Xiaojie], Zhang, C.Q.[Chang-Qing], Shi, H.L.[Hai-Lin], Li, S.Z.[Stan Z.],
Multi-view subspace clustering with intactness-aware similarity,
PR(88), 2019, pp. 50-63.
Elsevier DOI 1901
Intact space, Intactness-aware similarity, Multi-view subspace clustering BibRef

Tang, K.W.[Ke-Wei], Su, Z.[Zhixun], Liu, Y.[Yang], Jiang, W.[Wei], Zhang, J.[Jie], Sun, X.[Xiyan],
Subspace segmentation with a large number of subspaces using infinity norm minimization,
PR(89), 2019, pp. 45-54.
Elsevier DOI 1902
Subspace segmentation, Large subspace number, Infinity norm, Spectral-clustering based methods BibRef

Xia, Y., Zhang, Z.,
Scalable Feedback of Spectral Projection for Subspace Learning,
SPLetters(26), No. 2, February 2019, pp. 257-261.
IEEE DOI 1902
feedback, learning (artificial intelligence), matrix algebra, pattern clustering, scalable feedback, spectral projection, sign searching BibRef

Ashraphijuo, M.[Morteza], Wang, X.D.[Xiao-Dong],
Clustering a union of low-rank subspaces of different dimensions with missing data,
PRL(120), 2019, pp. 31-35.
Elsevier DOI 1904
Subspace clustering, Low-rank matrix completion, Union of subspaces BibRef

Li, B., Lu, H., Zhang, Y., Lin, Z., Wu, W.,
Subspace Clustering Under Complex Noise,
CirSysVideo(29), No. 4, April 2019, pp. 930-940.
IEEE DOI 1904
Clustering methods, Clustering algorithms, Data models, Correlation, Computer vision, Sparse matrices, expectation maximization BibRef

Abdolali, M.[Maryam], Rahmati, M.[Mohammad],
Robust subspace clustering for image data using clean dictionary estimation and group lasso based matrix completion,
JVCIR(61), 2019, pp. 303-314.
Elsevier DOI 1906
Subspace estimation, Sparse representation, Sparse subspace clustering, Group lasso, Matrix completion BibRef

Struski, L.[Lukasz], Spurek, P.[Przemyslaw], Tabor, J.[Jacek], Smieja, M.[Marek],
Projected memory clustering,
PRL(123), 2019, pp. 9-15.
Elsevier DOI 1906
Projected clustering, Subspaces clustering BibRef

Yi, S., Liang, Y., He, Z., Li, Y., Cheung, Y.,
Dual Pursuit for Subspace Learning,
MultMed(21), No. 6, June 2019, pp. 1399-1411.
IEEE DOI 1906
Dictionaries, Linear programming, Optimization, Feature extraction, Streaming media, Manifolds, Laplace equations, graph-regularization technique BibRef

Haralick, R.M.[Robert M.],
Dependence,
PRL(124), 2019, pp. 2-20.
Elsevier DOI 1906
Maximal correlation, Mutual information, Subspace classifiers BibRef

Guo, X.L.[Xiang-Lin], Xie, X.Y.[Xing-Yu], Liu, G.C.[Guang-Can], Wei, M.Q.[Ming-Qiang], Wang, J.[Jun],
Robust Low-rank subspace segmentation with finite mixture noise,
PR(93), 2019, pp. 55-67.
Elsevier DOI 1906
Subspace clustering, Noises modelling, Finite mixture model, Nonconvex and nonsmooth optimization BibRef

Zhang, Y.[Yong], Wang, Q.[Qi], Gong, D.W.[Dun-Wei], Song, X.F.[Xian-Fang],
Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection,
PR(93), 2019, pp. 337-352.
Elsevier DOI 1906
Unsupervised feature selection, Nonnegative Laplacian embedding, Subspace learning, Class labels BibRef

Tang, C., Zhu, X., Liu, X., Li, M., Wang, P., Zhang, C., Wang, L.,
Learning a Joint Affinity Graph for Multiview Subspace Clustering,
MultMed(21), No. 7, July 2019, pp. 1724-1736.
IEEE DOI 1906
Matrix converters, Data models, Clustering algorithms, Redundancy, Minimization, Optimization, Clustering methods, affinity graph learning BibRef

Liang, J.[Jie], Yang, J.F.[Ju-Feng], Cheng, M.M.[Ming-Ming], Rosin, P.L.[Paul L.], Wang, L.[Liang],
Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship,
IP(28), No. 8, August 2019, pp. 3973-3985.
IEEE DOI 1907
data structures, graph theory, matrix algebra, minimisation, pattern clustering, simultaneous subspace clustering, hyper-graph clustering BibRef

Yang, J.F.[Ju-Feng], Liang, J.[Jie], Wang, K.[Kai], Rosin, P.L.[Paul L.], Yang, M.H.[Ming-Hsuan],
Subspace Clustering via Good Neighbors,
PAMI(42), No. 6, June 2020, pp. 1537-1544.
IEEE DOI 2005
Clustering algorithms, Sparse matrices, Correlation, Clustering methods, Optimization, Minimization, graph connectivity BibRef

Mei, J., Wang, Y., Zhang, L., Zhang, B., Liu, S., Zhu, P., Ren, Y.,
PSASL: Pixel-Level and Superpixel-Level Aware Subspace Learning for Hyperspectral Image Classification,
GeoRS(57), No. 7, July 2019, pp. 4278-4293.
IEEE DOI 1907
Linear programming, Dimensionality reduction, Feature extraction, Hyperspectral imaging, Correlation, Learning systems, subspace learning BibRef

Chen, Z.Y.[Zheng-Yi], Zhang, C.M.[Chun-Min], Mu, T.[Tingkui], Yan, T.Y.[Ting-Yu], Chen, Z.[Zeyu], Wang, Y.Q.[Yan-Qiang],
An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Luo, J., Jiao, L., Liu, F., Yang, S., Ma, W.,
A Pareto-Based Sparse Subspace Learning Framework,
Cyber(49), No. 11, November 2019, pp. 3859-3872.
IEEE DOI 1908
Feature extraction, Optimization, Kernel, Dimensionality reduction, Task analysis, Linear programming, Evolutionary computation, subspace learning BibRef

Xue, Z., Du, J., Du, D., Li, G., Huang, Q., Lyu, S.,
Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification,
SPLetters(26), No. 8, August 2019, pp. 1177-1181.
IEEE DOI 1908
learning (artificial intelligence), matrix decomposition, pattern classification, semi-supervised classification BibRef

Yang, Z., Xu, Q., Zhang, W., Cao, X., Huang, Q.,
Split Multiplicative Multi-View Subspace Clustering,
IP(28), No. 10, October 2019, pp. 5147-5160.
IEEE DOI 1909
Clustering methods, Sparse matrices, Optimization, Computer vision, Periodic structures, Information security, Computer security, image representation BibRef

Liu, G., Zhang, Z., Liu, Q., Xiong, H.,
Robust Subspace Clustering With Compressed Data,
IP(28), No. 10, October 2019, pp. 5161-5170.
IEEE DOI 1909
Sparse matrices, Image coding, Sensors, Dimensionality reduction, Automation, Information science, Principal component analysis, low-rankness BibRef

Hechmi, S.[Sabra], Gallas, A.[Abir], Zagrouba, E.[Ezzeddine],
Multi-kernel sparse subspace clustering on the Riemannian manifold of symmetric positive definite matrices,
PRL(125), 2019, pp. 21-27.
Elsevier DOI 1909
Sparse subspace clustering (SSC), Riemannian kernel, Multi-kernel SSC, Face clustering BibRef

Dong, W.H.[Wen-Hua], Wu, X.J.[Xiao-Jun], Kittler, J.V.[Josef V.],
Sparse subspace clustering via smoothed lp minimization,
PRL(125), 2019, pp. 206-211.
Elsevier DOI 1909
Sparse subspace clustering, l minimization, Unified formulation, Alternating Direction Method BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Discriminative clustering using regularized subspace learning,
PR(96), 2019, pp. 106982.
Elsevier DOI 1909
Discriminative clustering, Subspace learning, Unsupervised learning BibRef

Huang, W.T.[Wei-Tian], Yin, M.[Ming], Li, J.Z.[Jian-Zhong], Xie, S.L.[Sheng-Li],
Deep Clustering via Weighted k-Subspace Network,
SPLetters(26), No. 11, November 2019, pp. 1628-1632.
IEEE DOI 1911
Feature extraction, Training, Clustering algorithms, Signal processing algorithms, Neural networks, Decoding, autoencoder BibRef

Su, C.C.[Chun-Chen], Wu, Z.Z.[Zong-Ze], Yin, M.[Ming], Li, K.X.[Kai-Xin], Sun, W.J.[Wei-Jun],
Subspace clustering via independent subspace analysis network,
ICIP17(4217-4221)
IEEE DOI 1803
Clustering algorithms, Computational modeling, Data models, Databases, Kernel, Machine learning, Task analysis, Subspace clustering BibRef

Jaberi, M.[Maryam], Pensky, M.[Marianna], Foroosh, H.[Hassan],
Sparse One-Grab Sampling with Probabilistic Guarantees,
PAMI(41), No. 12, December 2019, pp. 3057-3070.
IEEE DOI 1911
BibRef
Earlier:
Probabilistic Sparse Subspace Clustering Using Delayed Association,
ICPR18(2087-2092)
IEEE DOI 1812
Data models, Sociology, Computational modeling, Mathematical model, Sampling methods, Iterative methods, Sampling big data, subspace clustering. data mining, learning (artificial intelligence), optimisation, pattern clustering, probability, clustering subspaces, Computer science BibRef

Zhang, C.Q.[Chang-Qing], Fu, H.Z.[Hua-Zhu], Hu, Q.H.[Qing-Hua], Cao, X.C.[Xiao-Chun], Xie, Y.[Yuan], Tao, D.C.[Da-Cheng], Xu, D.[Dong],
Generalized Latent Multi-View Subspace Clustering,
PAMI(42), No. 1, January 2020, pp. 86-99.
IEEE DOI 1912
Clustering methods, Correlation, Electronic mail, Neural networks, Task analysis, Clustering algorithms, Minimization, neural networks BibRef

Zhang, C.Q.[Chang-Qing], Fu, H.Z.[Hua-Zhu], Liu, S.[Si], Liu, G., Cao, X.C.[Xiao-Chun],
Low-Rank Tensor Constrained Multiview Subspace Clustering,
ICCV15(1582-1590)
IEEE DOI 1602
Aerospace electronics BibRef

Li, R.H.[Rui-Huang], Zhang, C.Q.[Chang-Qing], Fu, H.Z.[Hua-Zhu], Peng, X.[Xi], Zhou, J.T.[Joey Tianyi], Hu, Q.H.[Qing-Hua],
Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering,
ICCV19(8171-8179)
IEEE DOI 2004
learning (artificial intelligence), optimisation, pattern clustering, multiview clustering, high-dimensional data, Computer vision BibRef

Cao, X.C.[Xiao-Chun], Zhang, C.Q.[Chang-Qing], Fu, H.Z.[Hua-Zhu], Liu, S.[Si], Zhang, H.[Hua],
Diversity-induced Multi-view Subspace Clustering,
CVPR15(586-594)
IEEE DOI 1510
BibRef

Zhang, C.Q.[Chang-Qing], Hu, Q.H.[Qing-Hua], Fu, H.Z.[Hua-Zhu], Zhu, P., Cao, X.C.[Xiao-Chun],
Latent Multi-view Subspace Clustering,
CVPR17(4333-4341)
IEEE DOI 1711
Clustering algorithms, Clustering methods, Erbium, Kernel, Linear programming, Optimization, Robustness BibRef

Chen, L.[Long], Zhong, Z.[Zhi],
Progressive graph-based subspace transductive learning for semi-supervised classification,
IET-IPR(13), No. 14, 12 December 2019, pp. 2753-2762.
DOI Link 1912
BibRef

Zhu, W.Q.[Wen-Qi], Yang, S.[Shuai], Zhu, Y.S.[Yue-Sheng],
Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering,
SPLetters(26), No. 12, December 2019, pp. 1892-1896.
IEEE DOI 2001
data analysis, iterative methods, matrix algebra, pattern clustering, RCOMP-SSC, sparse subspace clustering, sparse subspace clustering (SSC) BibRef

Zhong, L.[Li], Zhu, Y.S.[Yue-Sheng], Luo, G.[Guibo],
A New Sparse Subspace Clustering by Rotated Orthogonal Matching Pursuit,
ICIP18(3853-3857)
IEEE DOI 1809
Matching pursuit algorithms, Sparse matrices, Principal component analysis, Programming, Tools, Nonnegative Matrix Factorization BibRef

Yan, F.[Fei], Wang, X.D.[Xiao-Dong], Zeng, Z.Q.[Zhi-Qiang], Hong, C.Q.[Chao-Qun],
Adaptive multi-view subspace clustering for high-dimensional data,
PRL(130), 2020, pp. 299-305.
Elsevier DOI 2002
Subspace clustering, Multi-view clustering, Adaptive learning, Feature selection BibRef

Yu, E.[En], Li, J.[Jing], Wang, L.[Li], Zhang, J.[Jia], Wan, W.[Wenbo], Sun, J.[Jiande],
Multi-Class Joint Subspace Learning for Cross-Modal Retrieval,
PRL(130), 2020, pp. 165-173.
Elsevier DOI 2002
Multi-class, Cross-modal retrieval, Subspace learning BibRef

Xie, X.Y.[Xing-Yu], Guo, X.L.[Xiang-Lin], Liu, G.C.[Guang-Can], Wang, J.[Jun],
Implicit Block Diagonal Low-Rank Representation,
IP(27), No. 1, January 2018, pp. 477-489.
IEEE DOI 1712
Clustering algorithms, Clustering methods, Convergence, Kernel, Laplace equations, Optimization, Robustness, nonconvex optimization BibRef

Wan, Y., Zhong, Y., Ma, A., Zhang, L.,
Multi-Objective Sparse Subspace Clustering for Hyperspectral Imagery,
GeoRS(58), No. 4, April 2020, pp. 2290-2307.
IEEE DOI 2004
Sparse matrices, Optimization, Hyperspectral imaging, Dictionaries, TV, Hyperspectral image (HSI), multi-objective optimization, sparse subspace clustering (SSC) BibRef

Lipor, J.[John], Balzano, L.[Laura],
Clustering quality metrics for subspace clustering,
PR(104), 2020, pp. 107328.
Elsevier DOI 2005
Subspace clustering, Clustering validation, Union of subspaces BibRef

Zhang, B.B.[Bing-Bing], Wang, Q.L.[Qi-Long], Lu, X.X.[Xiao-Xiao], Wang, F.S.[Fa-Sheng], Li, P.H.[Pei-Hua],
Locality-constrained affine subspace coding for image classification and retrieval,
PR(100), 2020, pp. 107167.
Elsevier DOI 2005
BibRef
Earlier: A5, A3, A2, Only:
From dictionary of visual words to subspaces: Locality-constrained affine subspace coding,
CVPR15(2348-2357)
IEEE DOI 1510
Bag of visual words, Locality-constrained affine subspace coding, Image retrieval BibRef

Xie, D.[Deyan], Nie, F.P.[Fei-Ping], Gao, Q.X.[Quan-Xue], Xiao, S.[Song],
Fast algorithm for large-scale subspace clustering by LRR,
IET-IPR(14), No. 8, 19 June 2020, pp. 1475-1480.
DOI Link 2005
BibRef

Chen, Y.Y.[Yong-Yong], Xiao, X.L.[Xiao-Lin], Zhou, Y.C.[Yi-Cong],
Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix,
PR(106), 2020, pp. 107441.
Elsevier DOI 2006
Multi-view subspace clustering, Low-rank tensor representation, Local manifold BibRef


Ghojogh, B.[Benyamin], Karray, F.[Fakhri], Crowley, M.[Mark],
Generalized Subspace Learning by Roweis Discriminant Analysis,
ICIAR20(I:328-342).
Springer DOI 2007
BibRef

Kheirandishfard, M., Zohrizadeh, F., Kamangar, F.,
Multi-Level Representation Learning for Deep Subspace Clustering,
WACV20(2028-2037)
IEEE DOI 2006
Decoding, Clustering algorithms, Task analysis, Minimization, Face, Aggregates, Computer architecture BibRef

Lane, C., Boger, R., You, C., Tsakiris, M., Haeffele, B., Vidal, R.,
Classifying and Comparing Approaches to Subspace Clustering with Missing Data,
RSL-CV19(669-677)
IEEE DOI 2004
matrix algebra, pattern clustering, complementary problem, high-rank matrix completion, data point, representative methods, matrix completion BibRef

Wang, X.B.[Xiao-Bo], Guo, X.J.[Xiao-Jie], Lei, Z.[Zhen], Zhang, C.Q.[Chang-Qing], Li, S.Z.[Stan Z.],
Exclusivity-Consistency Regularized Multi-view Subspace Clustering,
CVPR17(1-9)
IEEE DOI 1711
Benchmark testing, Clustering algorithms, Conferences, Optimization, Pattern recognition, Standards BibRef

Yamaguchi, M., Irie, G., Kawanishi, T., Kashino, K.,
Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering,
ICCV19(9874-9883)
IEEE DOI 2004
expectation-maximisation algorithm, graph theory, image processing, matrix algebra, optimisation, pattern clustering, Sparse matrices BibRef

You, C., Li, C., Robinson, D., Vidal, R.,
Is an Affine Constraint Needed for Affine Subspace Clustering?,
ICCV19(9914-9923)
IEEE DOI 2004
pattern clustering, affinely independent subspaces, affine subspace clustering, subspace clustering methods, Data models BibRef

He, L., Yang, H., Zhao, L.,
Tensor Subspace Learning and Classification: Tensor Local Discriminant Embedding for Hyperspectral Image,
RSL-CV19(589-598)
IEEE DOI 2004
data reduction, geophysical image processing, graph theory, hyperspectral imaging, image classification, image resolution, local discriminant embedding BibRef

Rudolph, M., Wandt, B., Rosenhahn, B.,
Structuring Autoencoders,
RSL-CV19(615-623)
IEEE DOI 2004
image representation, learning (artificial intelligence), neural nets, structuring autoencoders, SAE, neural networks, Subspaces BibRef

Tang, K., Xu, K., Su, Z., Jiang, W., Luo, X., Sun, X.,
Structure-Constrained Feature Extraction by Autoencoders for Subspace Clustering,
RSL-CV19(624-632)
IEEE DOI 2004
feature extraction, learning (artificial intelligence), neural nets, pattern clustering, structured autoencoders, Structure Constrained Feature Extraction BibRef

Seo, J., Koo, J., Jeon, T.,
Deep Closed-Form Subspace Clustering,
RSL-CV19(633-642)
IEEE DOI 2004
learning (artificial intelligence), pattern clustering, closed-form shallow auto-encoder, deep subspace clustering BibRef

Gilman, K., Balzano, L.,
Panoramic Video Separation with Online Grassmannian Robust Subspace Estimation,
RSL-CV19(643-651)
IEEE DOI 2004
cameras, computer vision, gradient methods, object detection, principal component analysis, video signal processing, video foreground background separation BibRef

Li, Y.M.[Yuan-Man], Zhou, J.T.[Jian-Tao], Zheng, X.W.[Xian-Wei], Tian, J.[Jinyu], Tang, Y.Y.[Yuan Yan],
Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling,
CVPR19(8712-8721).
IEEE DOI 2002
BibRef

Wu, J., Huang, L., Yang, M., Chang, L., Liu, C.,
Sparse Subspace Clustering With Sequentially Ordered and Weighted L1-Minimization†,
ICIP19(3387-3391)
IEEE DOI 1910
Subspace clustering, sparse representation, compressive sensing BibRef

Carvalho, J., Marques, M., Costeira, J.P.,
Recovery of Subspace Structure from High-Rank Data with Missing Entries,
ICIP19(2010-2014)
IEEE DOI 1910
Motion Segmentation, Subspace Clustering, Missing Data, Matrix Completion, Sparse Representation BibRef

Chen, Y.,
Nonparametric Learning Via Successive Subspace Modeling (SSM),
ICIP19(3031-3032)
IEEE DOI 1910
Machine Learning, Explainable Machine Learning, Nonparametric Learning, Subspace Modeling, Successive Subspace Modeling BibRef

Hast, A.[Anders], Lind, M.[Mats], Vats, E.[Ekta],
Embedded Prototype Subspace Classification: A Subspace Learning Framework,
CAIP19(II:581-592).
Springer DOI 1909
BibRef

Fathy, M.E.[Mohammed E.], Alavi, A.[Azadeh], Chellappa, R.[Rama],
Nonlinear Subspace Feature Enhancement for Image Set Classification,
ACCV18(IV:142-158).
Springer DOI 1906
BibRef

Zhang, T.[Tong], Ji, P.[Pan], Harandi, M.[Mehrtash], Hartley, R.I.[Richard I.], Reid, I.D.[Ian D.],
Scalable Deep k-Subspace Clustering,
ACCV18(V:466-481).
Springer DOI 1906
BibRef

Zhou, P., Hou, Y., Feng, J.,
Deep Adversarial Subspace Clustering,
CVPR18(1596-1604)
IEEE DOI 1812
Generators, Clustering methods, Fasteners, Task analysis, Feeds, Estimation BibRef

Ma, L., Liu, Z.,
Hybrid Sparse Subspace Clustering for Visual Tracking,
ICPR18(1737-1742)
IEEE DOI 1812
Clustering methods, Principal component analysis, Visualization, Adaptation models, Motion segmentation, Object tracking BibRef

Zhang, Y., Wang, X., Gao, X.,
Adaptive Latent Representation for Multi-view Subspace Learning,
ICPR18(1229-1234)
IEEE DOI 1812
Clustering methods, Linear programming, Learning systems, Noise measurement, Optimization methods, Sparse matrices BibRef

Ye, Q., Zhang, Z.,
Rotational Invariant Discriminant Subspace Learning For Image Classification,
ICPR18(1217-1222)
IEEE DOI 1812
Principal component analysis, Minimization, Robustness, Iterative methods, Noise measurement, Linear matrix inequalities, s-norm minimization BibRef

Sznaier, M., Camps, O.,
SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms,
CVPR18(8033-8041)
IEEE DOI 1812
Optimization, Noise measurement, Level set, Reliability, Motion segmentation BibRef

Li, C., Zhang, J., Guo, J.,
Constrained Sparse Subspace Clustering with Side-Information,
ICPR18(2093-2099)
IEEE DOI 1812
Sparse matrices, Indexes, Data models, Task analysis, Clustering algorithms, Cancer, Gene expression BibRef

Zhou, L.[Lei], Wang, S.[Shuai], Bai, X.[Xiao], Zhou, J.[Jun], Hancock, E.R.[Edwin R.],
Iterative Deep Subspace Clustering,
SSSPR18(42-51).
Springer DOI 1810
BibRef

Wang, T., Cai, H., Zhang, X., Lan, L., Huang, X., Lu, Z.,
Graph-Laplacian Correlated Low-Rank Representation for Subspace Clustering,
ICIP18(3748-3752)
IEEE DOI 1809
Laplace equations, Correlation, Motion segmentation, Sparse matrices, Optimization, Computer vision, Manifolds, the self-expression BibRef

Panahi, A., Bian, X., Krim, H., Dai, L.,
Robust Subspace Clustering by Bi-Sparsity Pursuit: Guarantees and Sequential Algorithm,
WACV18(1302-1311)
IEEE DOI 1806
computer vision, concave programming, convex programming, image representation, pattern clustering, bi-sparsity pursuit, Uncertainty BibRef

Abavisani, M.[Mahdi], Patel, V.[Vishal],
Domain Adaptive Subspace Clustering,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Ackermann, H., Rosenhahn, B., Yang, M.Y.,
Unbiased Sparse Subspace Clustering by Selective Pursuit,
CRV17(1-7)
IEEE DOI 1804
pattern clustering, Dantzig selector, SSC, data points, linear subspaces, motion segmentation, selective pursuit, Subspace BibRef

Murdock, C.[Calvin], de la Torre, F.[Fernando],
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning,
ICCV17(4318-4326)
IEEE DOI 1802
computational geometry, concave programming, image representation, learning (artificial intelligence), Training BibRef

Gholami, B.[Behnam], Hajisami, A.[Abolfazl],
Probabilistic Semi-Supervised Multi-Modal Hashing,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Gholami, B., Pavlovic, V.,
Probabilistic Temporal Subspace Clustering,
CVPR17(4313-4322)
IEEE DOI 1711
Clustering algorithms, Computational modeling, Data models, Gaussian processes, Motion segmentation, Probabilistic, logic BibRef

Zhang, Y., Shi, D., Gao, J., Cheng, D.,
Low-Rank-Sparse Subspace Representation for Robust Regression,
CVPR17(2972-2981)
IEEE DOI 1711
Correlation, Data models, Input variables, Linear regression, Optimization, Robustness, Support, vector, machines BibRef

Chakraborty, R., Hauberg, S., Vemuri, B.C.,
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning,
CVPR17(801-809)
IEEE DOI 1711
Algorithm design and analysis, Distributed databases, Estimation, Manifolds, Optimization, Principal component analysis, Robustness BibRef

Wang, L.[Lei], Li, D.P.[Dan-Ping], He, T.C.[Tian-Cheng], Xue, Z.[Zhong],
Manifold Regularized Multi-view Subspace Clustering for image representation,
ICPR16(283-288)
IEEE DOI 1705
Clustering algorithms, Data models, Laplace equations, Manifolds, Optimization, Sparse matrices, Standards BibRef

Zheng, W.[Wei], Yan, H., Yang, J., Yang, J.,
Robust unsupervised feature selection by nonnegative sparse subspace learning,
ICPR16(3615-3620)
IEEE DOI 1705
Computational modeling, Data mining, Feature extraction, Optimization, Robustness, Sparse matrices, non-negative matrix factorization, subspace learning, unsupervised, feature, selection BibRef

Zheng, L.G.[Li-Gang], Qiu, G.P.[Guo-Ping], Huang, J.W.[Ji-Wu],
Clustering Symmetric Positive Definite Matrices on the Riemannian Manifolds,
ACCV16(I: 400-415).
Springer DOI 1704
BibRef

Zografos, V.[Vasileios], Krajsek, K.[Kai], Menze, B.[Bjoern],
An Online Algorithm for Efficient and Temporally Consistent Subspace Clustering,
ACCV16(I: 353-368).
Springer DOI 1704
BibRef

Tang, K.W.[Ke-Wei], Liu, X.D.[Xiao-Dong], Su, Z.X.[Zhi-Xun], Jiang, W.[Wei], Dong, J.X.[Jiang-Xin],
Subspace Learning Based Low-Rank Representation,
ACCV16(I: 416-431).
Springer DOI 1704
BibRef

Cao, G., Waris, M.A., Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Multi-modal subspace learning with dropout regularization for cross-modal recognition and retrieval,
IPTA16(1-6)
IEEE DOI 1703
eigenvalues and eigenfunctions BibRef

Zhang, J., Li, C.G., Zhang, H., Guo, J.,
Low-rank and structured sparse subspace clustering,
VCIP16(1-4)
IEEE DOI 1701
Clustering algorithms BibRef

You, C.[Chong], Robinson, D.P.[Daniel P.], Vidal, R.[René],
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit,
CVPR16(3918-3927)
IEEE DOI 1612
BibRef

You, C.[Chong], Li, C.[Chi], Robinson, D.P.[Daniel P.], Vidal, R.[René],
A Scalable Exemplar-Based Subspace Clustering Algorithm for Class-Imbalanced Data,
ECCV18(IX: 68-85).
Springer DOI 1810
BibRef

You, C.[Chong], Li, C.G.[Chun-Guang], Robinson, D.P.[Daniel P.], Vidal, R.[René],
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering,
CVPR16(3928-3937)
IEEE DOI 1612
BibRef

Yin, M., Guo, Y., Gao, J., He, Z., Xie, S.,
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds,
CVPR16(5157-5164)
IEEE DOI 1612
BibRef

Cheng, Y., Wang, Y., Sznaier, M., Camps, O.,
Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming,
CVPR16(5204-5212)
IEEE DOI 1612
BibRef

Yang, Y.Z.[Ying-Zhen], Feng, J.[Jiashi], Jojic, N.[Nebojsa], Yang, J.C.[Jian-Chao], Huang, T.S.[Thomas S.],
L0-Sparse Subspace Clustering,
ECCV16(II: 731-747).
Springer DOI 1611
BibRef

Gao, H., Nie, F., Li, X., Huang, H.,
Multi-view Subspace Clustering,
ICCV15(4238-4246)
IEEE DOI 1602
Benchmark testing BibRef

Ji, P., Salzmann, M., Li, H.,
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data,
ICCV15(4687-4695)
IEEE DOI 1602
Clustering algorithms BibRef

Wen, X., Qiao, L., Ma, S., Liu, W., Cheng, H.,
Sparse Subspace Clustering for Incomplete Images,
RSL-CV15(859-867)
IEEE DOI 1602
Clustering algorithms BibRef

Babaee, M.[Mohammadreza], Babaei, M.[Maryam], Merget, D.[Daniel], Tiefenbacher, P.[Philipp], Rigoll, G.[Gerhard],
Attribute constrained subspace learning,
ICIP15(3941-3945)
IEEE DOI 1512
Subspace learning BibRef

Silvestri, F.[Francesco], Reinelt, G.[Gerhard], Schnörr, C.[Christoph],
A Convex Relaxation Approach to the Affine Subspace Clustering Problem,
GCPR15(67-78).
Springer DOI 1511
BibRef

Yao, T.[Ting], Pan, Y.[Yingwei], Ngo, C.W.[Chong-Wah], Li, H.Q.[Hou-Qiang], Mei, T.[Tao],
Semi-supervised Domain Adaptation with Subspace Learning for visual recognition,
CVPR15(2142-2150)
IEEE DOI 1510
BibRef

Kim, E.[Eunwoo], Lee, M.[Minsik], Oh, S.H.[Song-Hwai],
Elastic-net regularization of singular values for robust subspace learning,
CVPR15(915-923)
IEEE DOI 1510
BibRef

Lee, M.[Minsik], Lee, J.[Jieun], Lee, H.[Hyeogjin], Kwak, N.[Nojun],
Membership representation for detecting block-diagonal structure in low-rank or sparse subspace clustering,
CVPR15(1648-1656)
IEEE DOI 1510
BibRef

Li, B.H.[Bao-Hua], Zhang, Y.[Ying], Lin, Z.C.[Zhou-Chen], Lu, H.C.[Hu-Chuan],
Subspace clustering by Mixture of Gaussian Regression,
CVPR15(2094-2102)
IEEE DOI 1510
BibRef

Yuan, X.T.[Xiao-Tong], Li, P.[Ping],
Sparse Additive Subspace Clustering,
ECCV14(III: 644-659).
Springer DOI 1408
See also Sparse Subspace Clustering: Algorithm, Theory, and Applications. BibRef

Peng, X.[Xi], Zhang, L.[Lei], Yi, Z.[Zhang],
Scalable Sparse Subspace Clustering,
CVPR13(430-437)
IEEE DOI 1309
Large scale dataset. Scalability and out-of-sample problems. See also Sparse Subspace Clustering: Algorithm, Theory, and Applications. BibRef

Tierney, S.[Stephen], Gao, J.B.[Jun-Bin], Guo, Y.[Yi],
Subspace Clustering for Sequential Data,
CVPR14(1019-1026)
IEEE DOI 1409
BibRef

Boulemnadjel, A.[Amel], Hachouf, F.[Fella],
Estimating Clusters Centres Using Support Vector Machine: An Improved Soft Subspace Clustering Algorithm,
CAIP13(254-261).
Springer DOI 1308
BibRef

Zografos, V.[Vasileios], Ellis, L.[Liam], Mester, R.[Rudolf],
Discriminative Subspace Clustering,
CVPR13(2107-2114)
IEEE DOI 1309
Discriminative clustering; Subspace clustering; quadratic classifier multiple classifiers on different parts of the data. BibRef

Lu, C.Y.[Can-Yi], Tang, J.H.[Jin-Hui], Lin, M.[Min], Lin, L.[Liang], Yan, S.C.[Shui-Cheng], Lin, Z.C.[Zhou-Chen],
Correntropy Induced L2 Graph for Robust Subspace Clustering,
ICCV13(1801-1808)
IEEE DOI 1403
BibRef

Nasihatkon, B.[Behrooz], Hartley, R.I.[Richard I.],
Graph connectivity in sparse subspace clustering,
CVPR11(2137-2144).
IEEE DOI 1106
Sparse Subspace Clustering (SSC). Connected for 2 or 3 dimensions, but not more. See also Sparse Subspace Clustering: Algorithm, Theory, and Applications. BibRef

Ke, Q.[Qifa], Kanade, T.,
Robust subspace clustering by combined use of kNND metric and SVD algorithm,
CVPR04(II: 592-599).
IEEE DOI 0408
Kth-Nearest-Neighbor, finds the clusters. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Distance Measures, Criteria for Clustering .


Last update:Jul 10, 2020 at 16:03:35