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.
WWW Link. 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 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, Two, dimensional, displays 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], 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, Two dimensional displays, 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


You, C., Robinson, D.P., Vidal, R.,
Provable Self-Representation Based Outlier Detection in a Union of Subspaces,
CVPR17(4323-4332)
IEEE DOI 1711
Anomaly detection, Markov processes, Principal component analysis, Robustness, Sparse matrices, Tools BibRef

Zhang, C., Hu, Q., Fu, H., Zhu, P., Cao, X.,
Latent Multi-view Subspace Clustering,
CVPR17(4333-4341)
IEEE DOI 1711
Clustering algorithms, Clustering methods, Erbium, Kernel, Linear programming, Optimization, Robustness 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

Wang, X., Guo, X., Lei, Z., Zhang, C., Li, S.Z.,
Exclusivity-Consistency Regularized Multi-view Subspace Clustering,
CVPR17(1-9)
IEEE DOI 1711
Benchmark testing, Clustering algorithms, Conferences, Optimization, Pattern recognition, Standards 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

Liu, H.J.[Hai-Jun], Cheng, J.[Jian], Wang, F.[Feng],
Sequential Subspace Clustering via Temporal Smoothness,
FG17(245-250)
IEEE DOI 1707
Clustering algorithms, Clustering methods, Dictionaries, Encoding, Face, Linear programming, Optimization 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., Robinson, D.P., Vidal, R.,
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit,
CVPR16(3918-3927)
IEEE DOI 1612
BibRef

You, C., Li, C.G., Robinson, D.P., Vidal, R.,
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

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

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

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

Li, P.H.[Pei-Hua], Lu, X.[Xiaoxiao], Wang, Q.[Qilong],
From dictionary of visual words to subspaces: Locality-constrained affine subspace coding,
CVPR15(2348-2357)
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:Nov 18, 2017 at 20:56:18