14.2.6.3 Subspace Clustering, Subspace Learning

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

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
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.Y.[Qi-Yue],
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.F.[Li-Fei], Wang, S.R.[Sheng-Rui], Wang, K.J.[Kai-Jun], 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

Washizawa, Y.[Yoshikazu],
Trace Norm Regularization and Application to Tensor Based Feature Extraction,
Subspace10(404-413).
Springer DOI 1109
BibRef

Washizawa, Y.[Yoshikazu], Tanaka, M.[Masayuki],
Centered Subset Kernel PCA for Denoising,
Subspace10(354-363).
Springer DOI 1109
BibRef

Washizawa, Y.[Yoshikazu],
Subset kernel PCA for pattern recognition,
Subspace09(162-169).
IEEE DOI 0910
BibRef

Washizawa, Y.[Yoshikazu], Yamashita, Y.[Yukihiko],
Non-linear Wiener filter in reproducing kernel Hilbert space,
ICPR06(I: 967-970).
IEEE DOI 0609
BibRef
Earlier:
Kernel sample space projection classifier for pattern recognition,
ICPR04(II: 435-438).
IEEE DOI 0409
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

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.M.[Da-Ming], Wang, J.[Jun], Cheng, D.[Dansong], Gao, J.B.[Jun-Bin],
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

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

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, 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.S.[Jia-Shi], 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

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, Minimization, Convergence, Optimization, Subspace clustering, convergence analysis BibRef

Tang, K.W.[Ke-Wei], Su, Z.X.[Zhi-Xun], 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, Sparse matrices, expectation maximization 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

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

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

Tzelepi, M.[Maria], Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Efficient Online Subclass Knowledge Distillation for Image Classification,
ICPR21(1007-1014)
IEEE DOI 2105
Training, Deep learning, Embedded systems, Computational modeling, Rendering (computer graphics), Image classification 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

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

Yu, E.[En], Li, J.[Jing], Wang, L.[Li], Zhang, J.[Jia], Wan, W.B.[Wen-Bo], 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.R.[Yue-Ru], Kuo, C.C.J.[C.C. Jay],
PixelHop: A successive subspace learning (SSL) method for object recognition,
JVCIR(70), 2020, pp. 102749.
Elsevier DOI 2007
Machine learning, Subspace learning BibRef

Chen, Y.R.[Yue-Ru], Rouhsedaghat, M., You, S., Rao, R., Kuo, C.C.J.[C.C. Jay],
Pixelhop++: A Small Successive-Subspace-Learning-Based (SSL-Based) Model For Image Classification,
ICIP20(3294-3298)
IEEE DOI 2011
Transforms, Correlation, Tensile stress, Computational modeling, Machine learning, Feature extraction, image classification BibRef

Shahi, K.R.[Kasra Rafiezadeh], Khodadadzadeh, M.[Mahdi], Tusa, L.[Laura], Ghamisi, P.[Pedram], Tolosana-Delgado, R.[Raimon], Gloaguen, R.[Richard],
Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Wang, Q., Lian, H., Sun, G., Gao, Q., Jiao, L.,
iCmSC: Incomplete Cross-Modal Subspace Clustering,
IP(30), 2021, pp. 305-317.
IEEE DOI 2012
Correlation, Kernel, Decoding, Clustering methods, Generative adversarial networks, Encoding, 2-norm BibRef

Yang, L.[Liran], Men, M.[Min], Xue, Y.M.[Yi-Ming], Wen, J.[Juan], Zhong, P.[Ping],
Transfer subspace learning based on structure preservation for JPEG image mismatched steganalysis,
SP:IC(90), 2021, pp. 116052.
Elsevier DOI 2012
Mismatch, Steganalysis, JPEG image, Transfer subspace learning, Structure preservation BibRef

Zheng, J.W.[Jian-Wei], Yang, P.[Ping], Shen, G.J.[Guo-Jiang], Chen, S.Y.[Sheng-Yong], Zhang, W.[Wei],
Enhanced low-rank constraint for temporal subspace clustering and its acceleration scheme,
PR(111), 2021, pp. 107678.
Elsevier DOI 2012
Subspace clustering, Majorization-minimization, Nonconvex surrogate function, Randomized singular value decomposition BibRef

Ren, J.F.[Jian-Feng], Jiang, X.D.[Xu-Dong],
A three-step classification framework to handle complex data distribution for radar UAV detection,
PR(111), 2021, pp. 107709.
Elsevier DOI 2012
Radar UAV detection, Micro-Doppler signature, Greedy subspace clustering, Subspace fusion BibRef

Xu, Y.[Yi], Liu, X.L.[Xiang-Long], Wang, B.S.[Bin-Shuai], Tao, R.S.[Ren-Shuai], Xia, K.[Ke], Cao, X.B.[Xian-Bin],
Fast Nearest Subspace Search via Random Angular Hashing,
MultMed(23), 2021, pp. 342-352.
IEEE DOI 2012
Large-scale search, nearest subspace search, locality-sensitive hash, linear subspace, subspace hashing BibRef

Xing, L.[Lei], Chen, B.D.[Ba-Dong], Wang, J.J.[Jian-Ji], Du, S.Y.[Shao-Yi], Cao, J.W.[Jiu-Wen],
Robust High-Order Manifold Constrained Low Rank Representation for Subspace Clustering,
CirSysVideo(31), No. 2, February 2021, pp. 533-545.
IEEE DOI 2102
Manifolds, Kernel, Laplace equations, Robustness, Iterative methods, Dictionaries, Low rank representation (LRR), robustness BibRef

Liu, H.[Hui], Wang, J.K.[Jin-Ke], Guo, D.M.[Dong-Mei], Fu, Y.Q.[Ya-Qing], Chen, S.[Song], Liu, S.[Shuang], Dan, G.[Guo],
Robust subspace clustering based on inter-cluster correlation reduction by low rank representation,
SP:IC(93), 2021, pp. 116137.
Elsevier DOI 2103
Subspace clustering, Inter-cluster correlation, Low rank model, Preprocessing BibRef

Peng, C.[Chong], Zhang, Q.[Qian], Kang, Z.[Zhao], Chen, C.L.[Cheng-Lizhao], Cheng, Q.[Qiang],
Kernel two-dimensional ridge regression for subspace clustering,
PR(113), 2021, pp. 107749.
Elsevier DOI 2103
Subspace clustering, Ridge regression, 2-dimensional, Kernel BibRef

Yang, S., Zhang, L., He, X., Yi, Z.,
Learning Manifold Structures With Subspace Segmentations,
Cyber(51), No. 4, April 2021, pp. 1981-1992.
IEEE DOI 2103
Clustering algorithms, Machine learning algorithms, Manifolds, Feature extraction, Linear programming, Dimensionality reduction, non-negative matrix factorization (NMF) BibRef

Xu, J.H.[Jin-Huan], Xiao, L.[Liang], Yang, J.X.[Jing-Xiang],
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Cai, Y.M.[Yao-Ming], Zhang, Z.J.[Zi-Jia], Cai, Z.H.[Zhi-Hua], Liu, X.B.[Xiao-Bo], Jiang, X.W.[Xin-Wei], Yan, Q.[Qin],
Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image,
GeoRS(59), No. 5, May 2021, pp. 4191-4202.
IEEE DOI 2104
Convolution, Data models, Robustness, Kernel, Sparse matrices, Geology, Atmospheric modeling, Graph convolutional networks (GCNs), subspace clustering BibRef

Cao, Y.[Yun], Mei, J.[Jie], Wang, Y.B.[Yue-Bin], Zhang, L.Q.[Li-Qiang], Peng, J.H.[Jun-Huan], Zhang, B.[Bing], Li, L.H.[Li-Hua], Zheng, Y.[Yibo],
SLCRF: Subspace Learning With Conditional Random Field for Hyperspectral Image Classification,
GeoRS(59), No. 5, May 2021, pp. 4203-4217.
IEEE DOI 2104
Linear programming, Training, Decoding, Hyperspectral imaging, Data models, Conditional random field (CRF), subspace learning (SL) BibRef

Hong, D.F.[Dan-Feng], Kang, J.[Jian], Yokoya, N.[Naoto], Chanussot, J.[Jocelyn],
Graph-Induced Aligned Learning on Subspaces for Hyperspectral and Multispectral Data,
GeoRS(59), No. 5, May 2021, pp. 4407-4418.
IEEE DOI 2104
Task analysis, Training, Kernel, Hyperspectral imaging, Manifolds, Cross-modality, fusion, graph learning, hyperspectral (HS), subspace learning BibRef

Wu, Z.Z.[Zong-Ze], Su, C.C.[Chun-Chen], Yin, M.[Ming], Ren, Z.G.[Zhi-Gang], Xie, S.L.[Sheng-Li],
Subspace clustering via stacked independent subspace analysis networks with sparse prior information,
PRL(146), 2021, pp. 165-171.
Elsevier DOI 2105
Subspace clustering, Independent subspace analysis, Low-dimensional representation, Feature selection BibRef

Lv, J.C.[Jun-Cheng], Kang, Z.[Zhao], Lu, X.[Xiao], Xu, Z.L.[Zeng-Lin],
Pseudo-Supervised Deep Subspace Clustering,
IP(30), 2021, pp. 5252-5263.
IEEE DOI 2106
Training, Image reconstruction, Clustering methods, Decoding, Manifolds, Task analysis, Robustness, Subspace clustering, pseudo-supervision BibRef

Liu, N.[Ning], Lai, Z.H.[Zhi-Hui], Li, X.C.[Xue-Chen], Chen, Y.D.[Yu-Dong], Mo, D.M.[Dong-Mei], Kong, H.[Heng], Shen, L.L.[Lin-Lin],
Locality Preserving Robust Regression for Jointly Sparse Subspace Learning,
CirSysVideo(31), No. 6, June 2021, pp. 2274-2287.
IEEE DOI 2106
Robustness, Feature extraction, Learning systems, Dimensionality reduction, Iterative methods, Training, subspace learning BibRef

Wu, F.F.[Fang-Fang], Yuan, P.[Peng], Shi, G.M.[Guang-Ming], Li, X.[Xin], Dong, W.S.[Wei-Sheng], Wu, J.J.[Jin-Jian],
Robust subspace clustering network with dual-domain regularization,
PRL(149), 2021, pp. 44-50.
Elsevier DOI 2108
Deep subspace clustering, Self-expressive clustering, Self-supervised clustering, Robust clustering, Dual domain regularization BibRef

Xu, Y.[Yesong], Chen, S.[Shuo], Li, J.[Jun], Luo, L.[Lei], Yang, J.[Jian],
Learnable low-rank latent dictionary for subspace clustering,
PR(120), 2021, pp. 108142.
Elsevier DOI 2109
Subspace clustering, Low-rank, Feature extraction, Block diagonal representation BibRef

Chakraborty, R.[Rudrasis], Yang, L.[Liu], Hauberg, S.[Søren], Vemuri, B.C.[Baba C.],
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning,
PAMI(43), No. 11, November 2021, pp. 3904-3917.
IEEE DOI 2110
BibRef
Earlier: A1, A3, A4, Only:
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning,
CVPR17(801-809)
IEEE DOI 1711
Principal component analysis, Kernel, Approximation algorithms, Manifolds, Distributed databases, Sparse matrices, Convergence, fréchet median. Algorithm design and analysis, Distributed databases, Estimation, Optimization, Robustness BibRef

Ye, X.L.[Xu-Lun], Luo, S.H.[Shu-Hui], Zhao, J.Y.[Jie-Yu],
Deep Bayesian Sparse Subspace Clustering,
SPLetters(28), 2021, pp. 1888-1892.
IEEE DOI 2110
Bayes methods, Clustering methods, Clustering algorithms, Task analysis, Laplace equations, Kernel, Inference algorithms, Bayesian learning BibRef

Wang, H.B.[Hui-Bing], Wang, Y.[Yang], Zhang, Z.[Zhao], Fu, X.P.[Xian-Ping], Zhuo, L.[Li], Xu, M.L.[Ming-Liang], Wang, M.[Meng],
Kernelized Multiview Subspace Analysis By Self-Weighted Learning,
MultMed(23), 2021, pp. 3828-3840.
IEEE DOI 2110
Kernel, Dimensionality reduction, Sparse matrices, Correlation, Optimization, Laplace equations, Image retrieval, Co-regularized, self-weighted BibRef

Mehrbani, E.[Eysan], Kahaei, M.H.[Mohammad Hossein], Beheshti, S.A.[Seyed Aliasghar],
Tensor Laplacian Regularized Low-Rank Representation for Non-Uniformly Distributed Data Subspace Clustering,
SPLetters(29), 2022, pp. 612-616.
IEEE DOI 2203
Tensors, Optimization, Image reconstruction, Laplace equations, Data structures, Geometry, Data models, Clustering, low rank, tensors BibRef

Li, Z.Z.[Zi-Zhuo], Ma, Y.[Yong], Mei, X.G.[Xiao-Guang], Huang, J.[Jun], Ma, J.Y.[Jia-Yi],
Guided neighborhood affine subspace embedding for feature matching,
PR(124), 2022, pp. 108489.
Elsevier DOI 2203
Feature matching, Image correspondence, Neighborhood affine subspace, Multi-scale, Outlier, Mismatch removal BibRef

Wan, M.H.[Ming-Hua], Yao, Y.[Yu], Zhan, T.M.[Tian-Ming], Yang, G.W.[Guo-Wei],
Supervised Low-Rank Embedded Regression (SLRER) for Robust Subspace Learning,
CirSysVideo(32), No. 4, April 2022, pp. 1917-1927.
IEEE DOI 2204
Manifold learning, Feature extraction, Matrix decomposition, Linear programming, Sparse matrices, 1-norm BibRef

Peng, Z.H.[Zhi-Hao], Jia, Y.H.[Yu-Heng], Liu, H.[Hui], Hou, J.H.[Jun-Hui], Zhang, Q.F.[Qing-Fu],
Maximum Entropy Subspace Clustering Network,
CirSysVideo(32), No. 4, April 2022, pp. 2199-2210.
IEEE DOI 2204
Sparse matrices, Couplings, Decoding, Clustering methods, Entropy, Kernel, Training, Deep learning, subspace clustering, decoupling BibRef

Peng, Z.H.[Zhi-Hao], Liu, H.[Hui], Jia, Y.H.[Yu-Heng], Hou, J.H.[Jun-Hui],
Adaptive Attribute and Structure Subspace Clustering Network,
IP(31), 2022, pp. 3430-3439.
IEEE DOI 2205
Feature extraction, Decoding, Kernel, Data mining, Adaptive systems, Symmetric matrices, Sparse matrices, Deep learning, structure information BibRef

Qin, Y.[Yalan], Wu, H.Z.[Han-Zhou], Zhao, J.[Jian], Feng, G.R.[Guo-Rui],
Enforced block diagonal subspace clustering with closed form solution,
PR(130), 2022, pp. 108791.
Elsevier DOI 2206
Subspace clustering, General form, Analytical, Nonnegative, Symmetrical solution BibRef

Hotta, K.[Katsuya], Xie, H.R.[Hao-Ran], Zhang, C.[Chao],
Component-based nearest neighbour subspace clustering,
IET-IPR(16), No. 10, 2022, pp. 2697-2708.
DOI Link 2207
BibRef

Zhou, J.H.[Jian-Hang], Zhang, B.[Bob], Zeng, S.N.[Shao-Ning], Lai, Q.[Qi],
Joint Discriminative Latent Subspace Learning for Image Classification,
CirSysVideo(32), No. 7, July 2022, pp. 4653-4666.
IEEE DOI 2207
Image reconstruction, Deep learning, Image classification, Training, Linear programming, Image representation, data representation BibRef

Kang, Z.[Zhao], Lin, Z.P.[Zhi-Ping], Zhu, X.F.[Xiao-Feng], Xu, W.B.[Wen-Bo],
Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview,
Cyber(52), No. 9, September 2022, pp. 8976-8986.
IEEE DOI 2208
Bipartite graph, Clustering methods, Periodic structures, Laplace equations, Clustering algorithms, Videos, Training, subspace clustering BibRef

Chen, Q.[Qiang], Zhao, X.W.[Xiao-Wei], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Fast Adaptive Local Subspace Learning With Regressive Regularization,
SPLetters(29), 2022, pp. 1759-1763.
IEEE DOI 2208
Adaptation models, Dimensionality reduction, Signal processing algorithms, Data models, Optics, Entropy, regressive regularization BibRef

Jiang, M.X.[Meng-Xi], Zhou, S.H.[Shi-Hao], Li, C.[Cuihua], Lei, Y.Q.[Yun-Qi],
JSL3d: Joint subspace learning with implicit structure supervision for 3D pose estimation,
PR(132), 2022, pp. 108965.
Elsevier DOI 2209
BibRef

Chen, J.[Jie], Yang, S.X.[Sheng-Xiang], Mao, H.[Hua], Fahy, C.[Conor],
Multiview Subspace Clustering Using Low-Rank Representation,
Cyber(52), No. 11, November 2022, pp. 12364-12378.
IEEE DOI 2211
Data models, Symmetric matrices, Probabilistic logic, Feature extraction, Clustering algorithms, Adaptation models, subspace clustering BibRef

Xu, Y.[Yesong], Chen, S.[Shuo], Li, J.[Jun], Xu, C.Y.[Chun-Yan], Yang, J.[Jian],
Fast subspace clustering by learning projective block diagonal representation,
PR(135), 2023, pp. 109152.
Elsevier DOI 2212
Subspace clustering, Block diagonal representation, Large-scale data BibRef

Li, Y.X.[Yu-Xuan], Li, X.[Xiang], Yang, J.[Jian],
Spatial Group-wise Enhance: Enhancing Semantic Feature Learning in CNN,
ACCV22(V:316-332).
Springer DOI 2307
BibRef

Tan, C.[Chao], Chen, S.[Sheng], Geng, X.[Xin], Ji, G.[Genlin],
A Novel Label Enhancement Algorithm Based on Manifold Learning,
PR(135), 2023, pp. 109189.
Elsevier DOI 2212
Multi-label learning, Label enhancement, Incremental subspace learning, Label propagation, Conditional random field BibRef

Qin, Y.L.[Ya-Lan], Zhang, X.P.[Xin-Peng], Shen, L.Q.[Li-Quan], Feng, G.R.[Guo-Rui],
Maximum Block Energy Guided Robust Subspace Clustering,
PAMI(45), No. 2, February 2023, pp. 2652-2659.
IEEE DOI 2301
Principal component analysis, Machine learning algorithms, Machine learning, Geometry, Clustering algorithms, iterative algorithm BibRef

Shi, Z.Y.[Zhao-Yin], Chen, L.[Long], Chen, G.Y.[Guang-Yong], Zhao, K.[Kai], Chen, C.L.P.[C. L. Philip],
Manifold Enhanced 2-D Fuzzy Subspace Clustering for Image Data,
SMCS(53), No. 2, February 2023, pp. 741-752.
IEEE DOI 2301
Feature extraction, Manifolds, Clustering methods, Computational modeling, Prototypes, Clustering algorithms, two-dimensional (2-D) feature extraction BibRef

Gan, M.[Min], Su, X.X.[Xiang-Xiang], Chen, G.Y.[Guang-Yong], Chen, J.[Jing], Chen, C.L.P.[C. L. Philip],
Online Learning Under a Separable Stochastic Approximation Framework,
PAMI(47), No. 2, February 2025, pp. 1317-1330.
IEEE DOI 2501
Stochastic processes, Approximation algorithms, Optimization, Machine learning algorithms, Machine learning, Convergence, variable projection BibRef

Jia, H.J.[Hong-Jie], Zhu, D.X.[Dong-Xia], Huang, L.X.[Long-Xia], Mao, Q.[Qirong], Wang, L.J.[Liang-Jun], Song, H.P.[He-Ping],
Global and local structure preserving nonnegative subspace clustering,
PR(138), 2023, pp. 109388.
Elsevier DOI 2303
Subspace clustering, Global structure, Local structure, Nonnegative Lagrangian relaxation, Kernel clustering BibRef

Lin, Y.L.[Yi-Ling], Lai, Z.H.[Zhi-Hui], Zhou, J.[Jie], Wen, J.J.[Jia-Jun], Kong, H.[Heng],
Multiview Jointly Sparse Discriminant Common Subspace Learning,
PR(138), 2023, pp. 109342.
Elsevier DOI 2303
Feature extraction, Small-class problem, Multiview classification, Discriminant common-space learning BibRef

Wang, L.[Libin], Wang, Y.L.[Yu-Long], Deng, H.[Hao], Chen, H.[Hong],
Attention reweighted sparse subspace clustering,
PR(139), 2023, pp. 109438.
Elsevier DOI 2304
Subspace clustering, Sparse, Robust representation BibRef

Lu, Y.[Yuwu], Wong, W.K.[Wai Keung], Zeng, B.Q.[Bi-Qing], Lai, Z.H.[Zhi-Hui], Li, X.L.[Xue-Long],
Guided Discrimination and Correlation Subspace Learning for Domain Adaptation,
IP(32), 2023, pp. 2017-2032.
IEEE DOI 2304
Correlation, Task analysis, Image classification, Artificial intelligence, Transfer learning BibRef

Cao, L.[Lei], Shi, L.[Long], Wang, J.[Jun], Yang, Z.D.[Zhen-Dong], Chen, B.D.[Ba-Dong],
Robust Subspace Clustering by Logarithmic Hyperbolic Cosine Function,
SPLetters(30), 2023, pp. 508-512.
IEEE DOI 2305
Clustering algorithms, Signal processing algorithms, Sparse matrices, Convergence, Behavioral sciences, Robustness, subspace clustering BibRef

Tang, Y.Q.[Yong-Qiang], Xie, Y.[Yuan], Zhang, W.[Wensheng],
Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor,
PAMI(45), No. 8, August 2023, pp. 9357-9373.
IEEE DOI 2307
Tensors, Sparse matrices, Task analysis, Motion segmentation, Clustering methods, Automation, System identification, subspace clustering BibRef

Zhao, Y.P.[Yin-Ping], Dai, X.F.[Xiang-Feng], Wang, Z.[Zhen], Li, X.L.[Xue-Long],
Subspace Clustering via Adaptive Non-Negative Representation Learning and Its Application to Image Segmentation,
CirSysVideo(33), No. 8, August 2023, pp. 4177-4189.
IEEE DOI 2308
Sparse matrices, Representation learning, Laplace equations, Optimization, Image segmentation, Data models, Clustering methods, graph learning BibRef

Fang, M.Z.[Meng-Zhu], Gao, W.[Wei], Feng, Z.[Zirui],
Deep robust multi-channel learning subspace clustering networks,
IVC(137), 2023, pp. 104769.
Elsevier DOI 2309
Subspace clustering, Deep networks, Unsupervised learning, Multi-channel learning, Convolutional auto-encoder, Feature extraction BibRef

Shen, Q.Q.[Qiang-Qiang], Yi, S.[Shuangyan], Liang, Y.S.[Yong-Sheng], Chen, Y.Y.[Yong-Yong], Liu, W.[Wei],
Bilateral Fast Low-Rank Representation With Equivalent Transformation for Subspace Clustering,
MultMed(25), 2023, pp. 6371-6383.
IEEE DOI 2311
BibRef

Wang, F.S.[Fu-Sheng], Chen, C.L.Z.[Cheng-Li-Zhao], Peng, C.[Chong],
Essential Low-Rank Sample Learning for Group-Aware Subspace Clustering,
SPLetters(30), 2023, pp. 1537-1541.
IEEE DOI 2311
BibRef

Zhang, H.[Hengmin], Li, S.Y.[Shu-Yi], Qiu, J.[Jing], Tang, Y.[Yang], Wen, J.[Jie], Zha, Z.Y.[Zhi-Yuan], Wen, B.[Bihan],
Efficient and Effective Nonconvex Low-Rank Subspace Clustering via SVT-Free Operators,
CirSysVideo(33), No. 12, December 2023, pp. 7515-7529.
IEEE DOI 2312
BibRef

Fu, H.Y.[Hong-Yu], Yang, Y.J.[Yi-Jing], Mishra, V.K.[Vinod K.], Kuo, C.C.J.[C.C. Jay],
Subspace learning machine (SLM): Methodology and performance evaluation,
JVCIR(98), 2024, pp. 104058.
Elsevier DOI 2402
Machine learning, Subspace learning, Classification, Regression BibRef

Zhang, X.Q.[Xiao-Qian], Zhao, S.[Shuai], Wang, J.[Jing], Guo, L.[Li], Wang, X.[Xiao], Sun, H.[Huaijiang],
Purity-Preserving Kernel Tensor Low-Rank Learning for Robust Subspace Clustering,
CirSysVideo(34), No. 3, March 2024, pp. 1900-1913.
IEEE DOI 2403
Kernel, Tensors, Clustering algorithms, Clustering methods, Hilbert space, Task analysis, Sparse matrices, noise segregation BibRef

Feng, W.[Wenyi], Wang, Z.[Zhe], Xiao, T.[Ting], Yang, M.[Mengping],
Adaptive weighted dictionary representation using anchor graph for subspace clustering,
PR(151), 2024, pp. 110350.
Elsevier DOI 2404
Dictionary representation, Subspace clustering, Anchor graph, Projection learning BibRef

Xu, Y.[Yesong], Hu, P.[Ping], Dai, J.[Jiashu], Yan, N.[Nan], Wang, J.[Jun],
Sparseness and Correntropy-Based Block Diagonal Representation for Robust Subspace Clustering,
SPLetters(31), 2024, pp. 1154-1158.
IEEE DOI 2405
Noise, Sparse matrices, Robustness, Clustering algorithms, Vectors, Optimization, Minimization, Block diagonal representation, noise BibRef

Wang, Y.B.[Yang-Bo], Zhou, J.[Jie], Lu, J.L.[Jiang-Lin], Wan, J.[Jun], Gao, C.[Can], Lin, Q.S.[Qing-Shui],
Robust Self-expression Learning with Adaptive Noise Perception,
PR(155), 2024, pp. 110695.
Elsevier DOI 2408
Self-expression, Noise perception, Representation learning, Subspace clustering BibRef

Liu, K.[Kangdao], Xiao, X.L.[Xiao-Lin], You, J.[Jinkun], Zhou, Y.C.[Yi-Cong],
Robust Discriminative t-Linear Subspace Learning for Image Feature Extraction,
CirSysVideo(34), No. 8, August 2024, pp. 7315-7327.
IEEE DOI 2408
Tensors, Feature extraction, Correlation, Vectors, Learning systems, Dimensionality reduction, Image reconstruction, t-product BibRef

Zhu, Y.J.[Yag-Jiao], Li, Q.[Qilin], Liu, W.Q.[Wan-Quan], Yin, C.[Chuancun],
Diffusion process with structural changes for subspace clustering,
PR(158), 2025, pp. 111066.
Elsevier DOI Code:
WWW Link. 2411
Subspace clustering, Diffusion process, Affinity learning, Dropout, Structural change BibRef

Wang, Q.[Qing], Ye, X.[Xulun], Wang, N.X.[Nong-Xiao],
Learning Low-Rank Representation Approximation for Few-Shot Deep Subspace Clustering,
CirSysVideo(34), No. 11, November 2024, pp. 10590-10603.
IEEE DOI 2412
Task analysis, Clustering methods, Metalearning, Sparse matrices, Semisupervised learning, Neural networks, Clustering algorithms, low-rank BibRef

Wei, L.[Lai], Chen, Z.W.[Zheng-Wei], Yin, J.[Jun], Zhu, C.M.[Chang-Ming], Zhou, R.[Rigui], Liu, J.[Jin],
Adaptive Graph Convolutional Subspace Clustering,
CVPR23(6262-6271)
IEEE DOI 2309
BibRef

Mochizuki, E.[Eri], Sone, H.[Haruka], Itoh, H.[Hayato], Imiya, A.[Atsushi],
Subspace Discrimination Method for Images Using Singular Value Decomposition,
ISVC21(II:287-298).
Springer DOI 2112
BibRef

Zhang, S.Z.[Shang-Zhi], You, C.[Chong], Vidal, R.[René], Li, C.G.[Chun-Guang],
Learning a Self-Expressive Network for Subspace Clustering,
CVPR21(12388-12398)
IEEE DOI 2111
Training, Computational modeling, Clustering methods, Neural networks, Training data, Stochastic processes BibRef

Valanarasu, J.M.J.[Jeya Maria Jose], Patel, V.M.[Vishal M.],
Overcomplete Deep Subspace Clustering Networks,
WACV21(746-755)
IEEE DOI 2106
Fuses, Clustering methods, Benchmark testing, Feature extraction, Data mining BibRef

Zeilmann, A.[Alexander], Petra, S.[Stefania], Schnörr, C.[Christoph],
Learning Linear Assignment Flows for Image Labeling via Exponential Integration,
SSVM21(385-397).
Springer DOI 2106
BibRef

Savarino, F.[Fabrizio], Albers, P.[Peter], Schnörr, C.[Christoph],
On the Geometric Mechanics of Assignment Flows for Metric Data Labeling,
SSVM21(398-410).
Springer DOI 2106
BibRef

He, Y.C.[Yi-Cong], Atia, G.K.[George K.],
Scalable Direction-Search-Based Approach to Subspace Clustering,
ICPR21(999-1006)
IEEE DOI 2105
Scalability, Clustering methods, Clustering algorithms, Partitioning algorithms, Computational complexity BibRef

Zhou, L.[Lei], Bai, X.[Xiao], Zhang, L.[Liang], Zhou, J.[Jun], Hancock, E.[Edwin],
Fast Subspace Clustering Based on the Kronecker Product,
ICPR21(1558-1565)
IEEE DOI 2105
Adaptation models, Computational modeling, Scalability, Clustering methods, Face recognition, Memory management, Data models BibRef

Dong, W.H.[Wen-Hua], Wu, X.J.[Xiao-Jun], Li, H.[Hui], Feng, Z.H.[Zhen-Hua], Kittler, J.V.[Josef V.],
Subspace Clustering via Joint Unsupervised Feature Selection,
ICPR21(3892-3898)
IEEE DOI 2105
Dictionaries, Clustering methods, Clustering algorithms, Feature extraction, Data mining, Optimization, Compressed sensing, half-quadratic BibRef

Yang, S.[Shuai], Zhu, W.Q.[Wen-Qi], Zhu, Y.S.[Yue-Sheng],
Sparse-Dense Subspace Clustering,
ICPR21(247-254)
IEEE DOI 2105
Correlation, Estimation, Benchmark testing, Sparse matrices, Iterative methods, Task analysis, Clustering, Unsupervised Learning BibRef

Zhan, J., Zhu, Y., Bai, Z.,
An Expression-Reinforced Sparse Subspace Clustering By Orthogonal Matching Pursuit,
ICIP20(211-215)
IEEE DOI 2011
sparse subspace clustering, signal processing, orthogonal matching pursuit, sparse representation BibRef

Zhan, J., Zhu, Y., Bai, Z.,
Targeted Incorporating Spatial Information in Sparse Subspace Clustering of Hyperspectral Remote Sensing Images,
ICIP20(2531-2535)
IEEE DOI 2011
Clustering algorithms, Clustering methods, Hyperspectral imaging, Sparse matrices, Computational complexity, sparse representation BibRef

Zhu, J., Zhang, T., Zhao, S.,
Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hypersepctral Imagery,
ICIP20(2860-2864)
IEEE DOI 2011
Image coding, Encoding, Apertures, Clustering algorithms, Imaging, Sensors, CASSI, spectral image clustering BibRef

Zhang, M., Wang, Y., Kadam, P., Liu, S., Kuo, C.C.J.[C.C. Jay],
Pointhop++: A Lightweight Learning Model on Point Sets for 3D Classification,
ICIP20(3319-3323)
IEEE DOI 2011
Transforms, Computational modeling, Feature extraction, Training, Tensile stress, Complexity theory, successive subspace learning. BibRef

Yang, M.L.[Mu-Li], Deng, C.[Cheng], Yan, J.C.[Jun-Chi], Liu, X.L.[Xiang-Long], Tao, D.C.[Da-Cheng],
Learning Unseen Concepts via Hierarchical Decomposition and Composition,
CVPR20(10245-10253)
IEEE DOI 2008
Visualization, Cats, Training, Feature extraction, Semantics, Task analysis, Adaptation models BibRef

Dang, Z., Deng, C., Yang, X., Huang, H.,
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint,
CVPR20(6657-6666)
IEEE DOI 2008
Feature extraction, Kernel, Data mining, Fuses, Training, Unsupervised learning, Motion segmentation BibRef

Kheirandishfard, M., Zohrizadeh, F., Kamangar, F.,
Deep Low-Rank Subspace Clustering,
Diff-CVML20(3776-3781)
IEEE DOI 2008
Decoding, Clustering algorithms, Data models, Kernel, Partitioning algorithms BibRef

Chen, Y., Li, C., You, C.,
Stochastic Sparse Subspace Clustering,
CVPR20(4154-4163)
IEEE DOI 2008
Optimization, Stochastic processes, Clustering methods, Matching pursuit algorithms, Data models, Clustering algorithms, Dictionaries BibRef

Tang, C., Yuan, L., Tan, P.,
LSM: Learning Subspace Minimization for Low-Level Vision,
CVPR20(6234-6245)
IEEE DOI 2008
Task analysis, Minimization, Optical imaging, Subspace constraints, Image segmentation, Training 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

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, 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

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

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, 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
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

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

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.S.[Jia-Shi], 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

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.W.[Ying-Wei], 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.F.[Qi-Fa], 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
Multi-View Subspace Clustering, Multi-View Subspace Learning .