14.1.4.3 Spectral Clustering, Data Dimensionality Reduction

Chapter Contents (Back)
Spectral Clustering. Use of the spectrum (eigenvalues) of the similarity matrix of the data for dimensionality reduction then cluster in lower dimension.
See also Graph Embedding Clustering.

Dhillon, I.S.[Inderjit S.], Guan, Y.Q.[Yu-Qiang], Kulis, B.[Brian],
Weighted Graph Cuts without Eigenvectors A Multilevel Approach,
PAMI(29), No. 11, November 2007, pp. 1944-1957.
IEEE DOI 0711
Analyze spectral clustering and kernel k-means -- both designed to cluster non linearly separable data -- to show the equivalence of the objective functions. Develop multi-level clustering. BibRef

Nagai, A.[Ayumu],
Inappropriateness of the criterion of k-way normalized cuts for deciding the number of clusters,
PRL(28), No. 15, 1 November 2007, pp. 1981-1986.
Elsevier DOI 0711
Spectral clustering; Number of clusters; Cluster validation BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Spectral clustering with eigenvector selection,
PR(41), No. 3, March 2008, pp. 1012-1029.
Elsevier DOI 0711
BibRef
Earlier:
Visual Learning Given Sparse Data of Unknown Complexity,
ICCV05(I: 701-708).
IEEE DOI 0510
Spectral clustering; Feature selection; Unsupervised learning; Image segmentation; Video behaviour pattern clustering BibRef

Alzate, C.[Carlos], Suykens, J.A.K.[Johan A.K.],
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA,
PAMI(32), No. 2, February 2010, pp. 335-347.
IEEE DOI 1001
PCA approach based on SVM formulation. BibRef

Ning, H.Z.[Hua-Zhong], Xu, W.[Wei], Chi, Y.[Yun], Gong, Y.H.[Yi-Hong], Huang, T.S.[Thomas S.],
Incremental spectral clustering by efficiently updating the eigen-system,
PR(43), No. 1, January 2010, pp. 113-127.
Elsevier DOI 0909
Incremental clustering; Spectral clustering; Incidence vector/matrix; Graph; Web-blogs BibRef

Chen, W.Y.[Wen-Yen], Song, Y.Q.[Yang-Qiu], Bai, H.J.[Hong-Jie], Lin, C.J.[Chih-Jen], Chang, E.Y.[Edward Y.],
Parallel Spectral Clustering in Distributed Systems,
PAMI(33), No. 3, March 2011, pp. 568-586.
IEEE DOI 1102
(from Yahoo, Microsoft and Google) Over a large set of documents and images. BibRef

Jia, J.H.[Jian-Hua], Xiao, X.[Xuan], Liu, B.X.[Bing-Xiang], Jiao, L.C.[Li-Cheng],
Bagging-based spectral clustering ensemble selection,
PRL(32), No. 10, 15 July 2011, pp. 1456-1467.
Elsevier DOI 1106
Spectral clustering; Selective clustering ensembles; Bagging; Normalized mutual information (NMI); Adjusted rand index (ARI) BibRef

Wang, L.[Liang], Leckie, C.[Christopher], Kotagiri, R.[Ramamohanarao], Bezdek, J.[James],
Approximate pairwise clustering for large data sets via sampling plus extension,
PR(44), No. 2, February 2011, pp. 222-235.
Elsevier DOI 1011
BibRef
Earlier: A1, A2, A3, Only:
Combining Real and Virtual Graphs to Enhance Data Clustering,
ICPR10(790-793).
IEEE DOI 1008
Pairwise data; Selective sampling; Spectral clustering; Graph embedding; Out-of-sample extension BibRef

Kim, J.W.[Jaeh-Wan], Choi, S.J.[Seung-Jin],
Semidefinite spectral clustering,
PR(39), No. 11, November 2006, pp. 2025-2035.
Elsevier DOI 0608
Convex optimization; Multi-way graph equipartitioning; Semidefinite programming; Spectral clustering BibRef

Shiga, M.[Motoki], Takigawa, I.[Ichigaku], Mamitsuka, H.[Hiroshi],
A spectral approach to clustering numerical vectors as nodes in a network,
PR(44), No. 2, February 2011, pp. 236-251.
Elsevier DOI 1011
Semi-supervised clustering; Heterogeneous data; Data integration; Spectral clustering BibRef

Shiga, M.[Motoki], Mamitsuka, H.[Hiroshi],
Efficient semi-supervised learning on locally informative multiple graphs,
PR(45), No. 3, March 2012, pp. 1035-1049.
Elsevier DOI 1111
Semi-supervised learning; Graph integration; Label propagation; Soft spectral clustering; EM (Expectation Maximization) algorithm BibRef

Yan, Y.[Yan], Shen, C.H.[Chun-Hua], Wang, H.Z.[Han-Zi],
Efficient Semidefinite Spectral Clustering via Lagrange Duality,
IP(23), No. 8, August 2014, pp. 3522-3534.
IEEE DOI 1408
convergence BibRef

Cao, J.Z.[Jiang-Zhong], Chen, P.[Pei], Dai, Q.Y.[Qing-Yun], Ling, W.K.[Wing-Kuen],
Local information-based fast approximate spectral clustering,
PRL(38), No. 1, 2014, pp. 63-69.
Elsevier DOI 1402
Spectral clustering BibRef

Lu, H.T.[Hong-Tao], Fu, Z.Y.[Zhen-Yong], Shu, X.[Xin],
Non-negative and sparse spectral clustering,
PR(47), No. 1, 2014, pp. 418-426.
Elsevier DOI 1310
Spectral clustering BibRef

David, G.[Gil], Averbuch, A.[Amir],
SpectralCAT: Categorical spectral clustering of numerical and nominal data,
PR(45), No. 1, 2012, pp. 416-433.
Elsevier DOI 1410
Spectral clustering BibRef

Xue, Z.H.[Zhao-Hui], Li, J.[Jun], Cheng, L.[Liang], Du, P.J.[Pei-Jun],
Spectral-Spatial Classification of Hyperspectral Data via Morphological Component Analysis-Based Image Separation,
GeoRS(53), No. 1, January 2015, pp. 70-84.
IEEE DOI 1410
Haar transforms BibRef

Xu, X.[Xiang], Li, J.[Jun], Li, S.T.[Shu-Tao], Plaza, A.[Antonio],
Subpixel Component Analysis for Hyperspectral Image Classification,
GeoRS(57), No. 8, August 2019, pp. 5564-5579.
IEEE DOI 1908
feature extraction, geophysical image processing, geophysical techniques, hyperspectral imaging, subpixel component analysis (SCA) BibRef

Shang, F.H.[Fan-Hua], Jiao, L.C., Shi, J.R.[Jia-Rong], Wang, F.[Fei], Gong, M.[Maoguo],
Fast affinity propagation clustering: A multilevel approach,
PR(45), No. 1, 2012, pp. 474-486.
Elsevier DOI 1410
both local and global structure information. BibRef

Tasdemir, K.[Kadim], Yalçin, B.[Berna], Yildirim, I.[Isa],
Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures,
PR(48), No. 4, 2015, pp. 1465-1477.
Elsevier DOI 1502
Approximate spectral clustering BibRef

Tasdemir, K.[Kadim], Moazzen, Y.[Yaser], Yildirim, I.[Isa],
Geodesic Based Similarities for Approximate Spectral Clustering,
ICPR14(1360-1364)
IEEE DOI 1412
Accuracy BibRef

Wang, H., Yuan, J.,
Collaborative Multifeature Fusion for Transductive Spectral Learning,
Cyber(45), No. 3, March 2015, pp. 465-475.
IEEE DOI 1502
Collaboration BibRef

Arzeno, N.M., Vikalo, H.,
Semi-Supervised Affinity Propagation with Soft Instance-Level Constraints,
PAMI(37), No. 5, May 2015, pp. 1041-1052.
IEEE DOI 1504
Availability BibRef

Yang, Y., Ma, Z., Yang, Y., Nie, F., Shen, H.T.,
Multitask Spectral Clustering by Exploring Intertask Correlation,
Cyber(45), No. 5, May 2015, pp. 1069-1080.
IEEE DOI 1505
Algorithm design and analysis BibRef

Cai, D., Chen, X.,
Large Scale Spectral Clustering Via Landmark-Based Sparse Representation,
Cyber(45), No. 8, August 2015, pp. 1669-1680.
IEEE DOI 1506
Algorithm design and analysis BibRef

Rahmani, M., Akbarizadeh, G.,
Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images,
IET-CV(9), No. 5, 2015, pp. 629-638.
DOI Link 1511
feature extraction BibRef

Eynard, D., Kovnatsky, A., Bronstein, M.M., Glashoff, K., Bronstein, A.M.,
Multimodal Manifold Analysis by Simultaneous Diagonalization of Laplacians,
PAMI(37), No. 12, December 2015, pp. 2505-2517.
IEEE DOI 1512
Laplace equations BibRef

Beauchemin, M.,
On affinity matrix normalization for graph cuts and spectral clustering,
PRL(68, Part 1), No. 1, 2015, pp. 90-96.
Elsevier DOI 1512
Affinity matrix BibRef

Alvarez-Meza, A.M., Castro-Ospina, A.E., Castellanos-Dominguez, G.,
Automatic graph pruning based on kernel alignment for spectral clustering,
PRL(70), No. 1, 2016, pp. 8-16.
Elsevier DOI 1602
Spectral clustering BibRef

Shang, R.H.[Rong-Hua], Zhang, Z.[Zhu], Jiao, L.C.[Li-Cheng], Wang, W.B.[Wen-Bing], Yang, S.Y.[Shu-Yuan],
Global discriminative-based nonnegative spectral clustering,
PR(55), No. 1, 2016, pp. 172-182.
Elsevier DOI 1604
Spectral clustering BibRef

Lu, C., Yan, S., Lin, Z.,
Convex Sparse Spectral Clustering: Single-View to Multi-View,
IP(25), No. 6, June 2016, pp. 2833-2843.
IEEE DOI 1605
Clustering algorithms BibRef

Wei, L.[Lai], Wang, X.F.[Xiao-Feng], Yin, J.[Jun], Wu, A.[Aihua],
Spectral clustering steered low-rank representation for subspace segmentation,
JVCIR(38), No. 1, 2016, pp. 386-395.
Elsevier DOI 1605
Subspace segmentation BibRef

Gilboa, G.[Guy], Moeller, M.[Michael], Burger, M.[Martin],
Nonlinear Spectral Analysis via One-Homogeneous Functionals: Overview and Future Prospects,
JMIV(56), No. 2, October 2016, pp. 300-319.
WWW Link. 1609
BibRef

Burger, M.[Martin], Gilboa, G.[Guy], Moeller, M.[Michael], Eckardt, L.[Lina], Cremers, D.[Daniel],
Spectral Decompositions Using One-Homogeneous Functionals,
SIIMS(9), No. 3, 2016, pp. 1374-1408.
DOI Link 1610
BibRef
Earlier: A1, A4, A2, A3, Only:
Spectral Representations of One-Homogeneous Functionals,
SSVM15(16-27).
Springer DOI 1506
BibRef

Li, Q.L.[Qi-Lin], Ren, Y.[Yan], Li, L.[Ling], Liu, W.Q.[Wan-Quan],
Fuzzy based affinity learning for spectral clustering,
PR(60), No. 1, 2016, pp. 531-542.
Elsevier DOI 1609
Similarity measure BibRef

Li, Q.L.[Qi-Lin], Liu, W.Q.[Wan-Quan], Li, L.[Ling],
Affinity learning via a diffusion process for subspace clustering,
PR(84), 2018, pp. 39-50.
Elsevier DOI 1809
Subspace clustering, Diffusion process, Affinity learning BibRef

Wang, H.X.[Hong-Xing], Kawahara, Y.[Yoshinobu], Weng, C.Q.[Chao-Qun], Yuan, J.S.[Jun-Song],
Representative Selection with Structured Sparsity,
PR(63), No. 1, 2017, pp. 268-278.
Elsevier DOI 1612
Representative selection BibRef

Wang, H.X.[Hong-Xing], Weng, C.Q.[Chao-Qun], Yuan, J.S.[Jun-Song],
Multi-feature Spectral Clustering with Minimax Optimization,
CVPR14(4106-4113)
IEEE DOI 1409
BibRef

Langone, R.[Rocco], van Barel, M.[Marc], Suykens, J.A.K.[Johan A.K.],
Efficient evolutionary spectral clustering,
PRL(84), No. 1, 2016, pp. 78-84.
Elsevier DOI 1612
Evolutionary spectral clustering BibRef

Li, P.[Ping], Ji, H.F.[Hai-Feng], Wang, B.L.[Bao-Liang], Huang, Z.Y.[Zhi-Yao], Li, H.Q.[Hai-Qing],
Adjustable preference affinity propagation clustering,
PRL(85), No. 1, 2017, pp. 72-78.
Elsevier DOI 1612
Pattern recognition BibRef

Chen, J., Li, Z., Huang, B.,
Linear Spectral Clustering Superpixel,
IP(26), No. 7, July 2017, pp. 3317-3330.
IEEE DOI 1706
Algorithm design and analysis, Clustering algorithms, Computational complexity, Image segmentation, Kernel, Linear programming, Shape, Superpixel, boundary adherence, compactness, normalized cuts, weighted, K-means, clustering BibRef

Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., Wang, C.,
Graph PCA Hashing for Similarity Search,
MultMed(19), No. 9, September 2017, pp. 2033-2044.
IEEE DOI 1708
Big Data, Binary codes, Manifolds, Principal component analysis, Time complexity, Training, Hashing, image retrieval, manifold learning, similarity search, spectral clustering BibRef

Jia, Y., Kwong, S., Hou, J.,
Semi-Supervised Spectral Clustering With Structured Sparsity Regularization,
SPLetters(25), No. 3, March 2018, pp. 403-407.
IEEE DOI 1802
Clustering algorithms, Clustering methods, Convergence, Eigenvalues and eigenfunctions, Mutual information, Optimization, spectral clustering (SC) BibRef

Kanaan-Izquierdo, S.[Samir], Ziyatdinov, A.[Andrey], Perera-Lluna, A.[Alexandre],
Multiview and multifeature spectral clustering using common eigenvectors,
PRL(102), 2018, pp. 30-36.
Elsevier DOI 1802
Multiview data, Spectral clustering, Common eigenvectors BibRef

Vora, A.[Aditya], Raman, S.[Shanmuganathan],
Iterative spectral clustering for unsupervised object localization,
PRL(106), 2018, pp. 27-32.
Elsevier DOI 1804
Object localization, Spectral clustering, Unsupervised localization BibRef

Gao, F.[Feng], Wang, Q.[Qun], Dong, J.Y.[Jun-Yu], Xu, Q.Z.[Qi-Zhi],
Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

He, L., Ray, N., Guan, Y., Zhang, H.,
Fast Large-Scale Spectral Clustering via Explicit Feature Mapping,
Cyber(49), No. 3, March 2019, pp. 1058-1071.
IEEE DOI 1902
Kernel, Time complexity, Clustering algorithms, Task analysis, Approximation algorithms, Matrix decomposition, Kernel matrix, spectral clustering BibRef

Zhao, Y.[Yang], Yuan, Y.[Yuan], Wang, Q.[Qi],
Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Zhang, R., Nie, F., Guo, M., Wei, X., Li, X.,
Joint Learning of Fuzzy k-Means and Nonnegative Spectral Clustering With Side Information,
IP(28), No. 5, May 2019, pp. 2152-2162.
IEEE DOI 1903
fuzzy set theory, interpolation, learning (artificial intelligence), pattern clustering, adaptive loss function BibRef

Alshammari, M.[Mashaan], Takatsuka, M.[Masahiro],
Approximate spectral clustering with eigenvector selection and self-tuned k,
PRL(122), 2019, pp. 31-37.
Elsevier DOI 1904
Spectral clustering, Approximate spectral clustering, Growing neural gas, Image segmentation BibRef

Wu, J., Lin, Z., Zha, H.,
Essential Tensor Learning for Multi-View Spectral Clustering,
IP(28), No. 12, December 2019, pp. 5910-5922.
IEEE DOI 1909
Markov processes, Learning systems, Correlation, Computational complexity, Clustering methods, Standards, tensor SVD BibRef

Yang, X.J.[Xiao-Jun], Yu, W.Z.[Wei-Zhong], Wang, R.[Rong], Zhang, G.H.[Guo-Hao], Nie, F.P.[Fei-Ping],
Fast spectral clustering learning with hierarchical bipartite graph for large-scale data,
PRL(130), 2020, pp. 345-352.
Elsevier DOI 2002
Spectral clustering, Hierarchical graph, Bipartite graph, Large scale data, Out-of-sample BibRef

Zhang, H.[Han], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Large-Scale Clustering With Structured Optimal Bipartite Graph,
PAMI(45), No. 8, August 2023, pp. 9950-9963.
IEEE DOI 2307
Bipartite graph, Scalability, Task analysis, Clustering algorithms, Optimization, Laplace equations, Partitioning algorithms, pairwise relation BibRef

Wang, Z.[Zhen], Li, Z.Q.[Zhao-Qing], Wang, R.[Rong], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Large Graph Clustering With Simultaneous Spectral Embedding and Discretization,
PAMI(43), No. 12, December 2021, pp. 4426-4440.
IEEE DOI 2112
Clustering methods, Clustering algorithms, Optimization, Complexity theory, Acceleration, Optical imaging, label propagation BibRef

Wen, J.[Jie], Xu, Y.[Yong], Liu, H.[Hong],
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning,
Cyber(50), No. 4, April 2020, pp. 1418-1429.
IEEE DOI 2003
Clustering methods, Laplace equations, Cybernetics, Diseases, Optimization, Clustering algorithms, Matrix decomposition, low-rank representation BibRef

Zhao, S.P.[Shu-Ping], Fei, L.[Lunke], Wen, J.[Jie], Wu, J.G.[Ji-Gang], Zhang, B.[Bob],
Intrinsic and Complete Structure Learning Based Incomplete Multiview Clustering,
MultMed(25), 2023, pp. 1098-1110.
IEEE DOI 2305
Sparse matrices, Representation learning, Optimization, Clustering methods, Task analysis, Correlation, Indexes, intrinsic structure learning BibRef

Wen, J.[Jie], Liu, C.L.[Cheng-Liang], Xu, G.[Gehui], Wu, Z.H.[Zhi-Hao], Huang, C.[Chao], Fei, L.[Lunke], Xu, Y.[Yong],
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-view Clustering,
CVPR23(15712-15721)
IEEE DOI 2309
BibRef

Zhu, X.F.[Xiao-Feng], Zhu, Y.H.[Yong-Hua], Zheng, W.[Wei],
Spectral rotation for deep one-step clustering,
PR(105), 2020, pp. 107175.
Elsevier DOI 2006
Similarity matrix learning, Spectral clustering, One-step clustering, Alternating direction method of multipliers BibRef

Tong, T.[Tao], Gan, J.Z.[Jiang-Zhang], Wen, G.Q.[Guo-Qiu], Li, Y.D.[Yang-Ding],
One-step spectral clustering based on self-paced learning,
PRL(135), 2020, pp. 8-14.
Elsevier DOI 2006
Missing value, Self-paced learning, One-step spectral clustering BibRef

Cheng, X.Y.[Xiu-Yuan], Mishne, G.[Gal],
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian,
SIIMS(13), No. 2, 2020, pp. 1015-1048.
DOI Link 2007
BibRef

Chen, X.J.[Xiao-Jun], Hong, W.J.[Wei-Jun], Nie, F.P.[Fei-Ping], Huang, J.Z.X.[Joshua Zhe-Xue], Shen, L.[Li],
Enhanced Balanced Min Cut,
IJCV(128), No. 7, July 2020, pp. 1982-1995.
Springer DOI 2007
BibRef

Hao, W., Pang, S., Zhu, J., Li, Y.,
Self-Weighting and Hypergraph Regularization for Multi-view Spectral Clustering,
SPLetters(27), 2020, pp. 1325-1329.
IEEE DOI 2008
Laplace equations, Adaptation models, Benchmark testing, Robustness, Linear programming, Closed-form solutions, Data models, multi-view BibRef

Affeldt, S.[Séverine], Labiod, L.[Lazhar], Nadif, M.[Mohamed],
Spectral clustering via ensemble deep autoencoder learning (SC-EDAE),
PR(108), 2020, pp. 107522.
Elsevier DOI 2008
Spectral clustering, Unsupervised ensemble learning, Autoencoder, BibRef

Affeldt, S.[Séverine], Labiod, L.[Lazhar], Nadif, M.[Mohamed],
CAEclust: A Consensus of Autoencoders Representations for Clustering,
IPOL(12), 2022, pp. 590-603.
DOI Link 2301
Code, Spectral Clustering. BibRef

Ye, X., Zhao, J., Chen, Y., Guo, L.,
Bayesian Adversarial Spectral Clustering With Unknown Cluster Number,
IP(29), 2020, pp. 8506-8518.
IEEE DOI 2008
Spectral clustering, Bayesian learning, low rank, variational inference, generative adversarial network BibRef

Alshammari, M.[Mashaan], Stavrakakis, J.[John], Takatsuka, M.[Masahiro],
Refining a k-nearest neighbor graph for a computationally efficient spectral clustering,
PR(114), 2021, pp. 107869.
Elsevier DOI 2103
Spectral clustering, Approximate spectral clustering, -nearest neighbor graph, Local scale similarity BibRef

Zhang, S.[Shukun], Murphy, J.M.[James M.],
Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Yan, Y.J.[Yu-Jia], Wu, G.X.[Guang-Xin], Dong, Y.[Yang], Bai, Y.C.[Ye-Chao],
An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing,
SPLetters(28), 2021, pp. 444-448.
IEEE DOI 2103
MSR: mean spectral radius. Smoothing methods, Eigenvalues and eigenfunctions, Correlation, Optimized production technology, Autoregressive processes, random matrix theory BibRef

Ge, Y.[Yan], Peng, P.[Pan], Lu, H.P.[Hai-Ping],
Mixed-order spectral clustering for complex networks,
PR(117), 2021, pp. 107964.
Elsevier DOI 2106
Spectral clustering, Higher-order structures, Mixed-order structures BibRef

Wang, Z.L.[Zhen-Lei], Zhao, S.[Suyun], Li, Z.[Zheng], Chen, H.[Hong], Li, C.P.[Cui-Ping], Shen, Y.F.[Yu-Feng],
Ensemble selection with joint spectral clustering and structural sparsity,
PR(119), 2021, pp. 108061.
Elsevier DOI 2106
Ensemble selection, Structural sparsity, Unsupervised selection, Spectral clustering, Robustness BibRef

Li, K.[Kang], Xu, J.D.[Jin-Dong], Zhao, T.Y.[Tian-Yu], Liu, Z.W.[Zhao-Wei],
A fuzzy spectral clustering algorithm for hyperspectral image classification,
IET-IPR(15), No. 12, 2021, pp. 2810-2817.
DOI Link 2109
BibRef

Peng, H.[Hong], Wang, H.Y.[Hai-Yan], Hu, Y.[Yu], Zhou, W.W.[Wei-Wei], Cai, H.M.[Hong-Min],
Multi-dimensional clustering through fusion of high-order similarities,
PR(121), 2022, pp. 108108.
Elsevier DOI 2109
High-order similarity, Low-rank, Multi-dimensional clustering, Spectral clustering BibRef

Peng, H.[Hong], Hu, Y.[Yu], Chen, J.Z.[Jia-Zhou], Wang, H.Y.[Hai-Yan], Li, Y.[Yang], Cai, H.M.[Hong-Min],
Integrating Tensor Similarity to Enhance Clustering Performance,
PAMI(44), No. 5, May 2022, pp. 2582-2593.
IEEE DOI 2204
Tensors, Matrix decomposition, Laplace equations, Clustering algorithms, Task analysis, Noise measurement, Manifolds, unsupervised learning BibRef

Jia, Y.H.[Yu-Heng], Liu, H.[Hui], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], Zhang, Q.F.[Qing-Fu],
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation,
CirSysVideo(31), No. 12, December 2021, pp. 4784-4797.
IEEE DOI 2112
Tensors, Sparse matrices, Symmetric matrices, Matrix decomposition, Urban areas, Feature extraction, tensor low-rank norm BibRef

Jia, Y.H.[Yu-Heng], Lu, G.X.[Guan-Xing], Liu, H.[Hui], Hou, J.H.[Jun-Hui],
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation,
CirSysVideo(33), No. 7, July 2023, pp. 3455-3461.
IEEE DOI 2307
Tensors, Clustering methods, Sparse matrices, Laplace equations, Geometry, Computer science, Urban areas, pairwise constraints BibRef

Shi, S.J.[Shao-Jun], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Self-weighting multi-view spectral clustering based on nuclear norm,
PR(124), 2022, pp. 108429.
Elsevier DOI 2203
Unsupervised learning, Multi-view clustering, Nuclear norm, Self-weighting BibRef

Yang, H.Z.[Hai-Zhou], Gao, Q.X.[Quan-Xue], Xia, W.[Wei], Yang, M.[Ming], Gao, X.B.[Xin-Bo],
Multiview Spectral Clustering With Bipartite Graph,
IP(31), 2022, pp. 3591-3605.
IEEE DOI 2206
Tensors, Matrix decomposition, Laplace equations, Clustering algorithms, Computational modeling, Adaptation models, large scale data BibRef

Xia, W.[Wei], Gao, Q.X.[Quan-Xue], Wang, Q.Q.[Qian-Qian], Gao, X.B.[Xin-Bo], Ding, C.[Chris], Tao, D.C.[Da-Cheng],
Tensorized Bipartite Graph Learning for Multi-View Clustering,
PAMI(45), No. 4, April 2023, pp. 5187-5202.
IEEE DOI 2303
Bipartite graph, Tensors, Laplace equations, Clustering methods, Sparse matrices, Clustering algorithms, Minimization, tensor schatten p-norm BibRef

Sun, G.[Gan], Cong, Y.[Yang], Dong, J.H.[Jia-Hua], Liu, Y.Y.[Yu-Yang], Ding, Z.M.[Zheng-Ming], Yu, H.B.[Hai-Bin],
What and How: Generalized Lifelong Spectral Clustering via Dual Memory,
PAMI(44), No. 7, July 2022, pp. 3895-3908.
IEEE DOI 2206
Task analysis, Correlation, Encoding, Clustering algorithms, Semantics, Robots, Refining, Lifelong machine learning, neural networks BibRef

Bai, L.[Liang], Zhao, Y.X.[Yun-Xiao], Liang, J.[Jiye],
Self-supervised spectral clustering with exemplar constraints,
PR(132), 2022, pp. 108975.
Elsevier DOI 2209
Spectral clustering, Self-supervised algorithm, Exemplar constraint, Optimization model BibRef

Bai, L.[Liang], Qi, M.[Minxue], Liang, J.[Jiye],
Spectral clustering with robust self-learning constraints,
AI(320), 2023, pp. 103924.
Elsevier DOI 2306
Cluster analysis, Spectral clustering, Self-learning constraints, Robustness BibRef

Zhang, F.P.[Fu-Ping], Zhao, J.[Jieyu], Ye, X.L.[Xu-Lun], Chen, H.[Hao],
One-Step Adaptive Spectral Clustering Networks,
SPLetters(29), 2022, pp. 2263-2267.
IEEE DOI 2212
Mathematical models, Clustering algorithms, Signal processing algorithms, Matrix decomposition, spectral rotation BibRef

Yang, G.P.[Ge-Ping], Deng, S.C.[Su-Cheng], Chen, X.[Xiang], Chen, C.[Can], Yang, Y.Y.[Yi-Yang], Gong, Z.G.[Zhi-Guo], Hao, Z.F.[Zhi-Feng],
RESKM: A General Framework to Accelerate Large-Scale Spectral Clustering,
PR(137), 2023, pp. 109275.
Elsevier DOI 2302
Machine learning, Spectral clustering, Unsupervised learning, Large-scale BibRef

Mei, Y.Y.[Yan-Ying], Ren, Z.N.[Zhe-Nwen], Wu, B.[Bin], Yang, T.[Tao], Shao, Y.H.[Yan-Hua],
Multi-order similarity learning for multi-view spectral clustering,
PR(137), 2023, pp. 109264.
Elsevier DOI 2302
Spectral clustering, Multi-view clustering, Multi-order similarity, Graph learning, Tensor BibRef

Bai, L.[Liang], Liang, J.[Jiye], Zhao, Y.X.[Yun-Xiao],
Self-Constrained Spectral Clustering,
PAMI(45), No. 4, April 2023, pp. 5126-5138.
IEEE DOI 2303
Clustering algorithms, Optimization, Linear programming, Kernel, Computational efficiency, Neural networks, Deep learning, spectral clustering BibRef

Zhong, G.[Guo], Pun, C.M.[Chi-Man],
Self-taught Multi-view Spectral Clustering,
PR(138), 2023, pp. 109349.
Elsevier DOI 2303
Graph clustering, spectral rotation, spectral clustering, multi-view clustering BibRef

Lu, Z.M.[Zhou-Min], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
A Differentiable Perspective for Multi-View Spectral Clustering With Flexible Extension,
PAMI(45), No. 6, June 2023, pp. 7087-7098.
IEEE DOI 2305
Deep learning, Kernel, Clustering algorithms, Optimization, Neural networks, Training, Visualization, Multi-view learning, differentiable programming BibRef

Li, Q.L.[Qi-Lin], An, S.J.[Sen-Jian], Li, L.[Ling], Liu, W.Q.[Wan-Quan], Shao, Y.[Yanda],
Multi-View Diffusion Process for Spectral Clustering and Image Retrieval,
IP(32), 2023, pp. 4610-4620.
IEEE DOI 2309
BibRef

Zhao, M.Y.[Ming-Yu], Yang, W.D.[Wei-Dong], Nie, F.P.[Fei-Ping],
Deep multi-view spectral clustering via ensemble,
PR(144), 2023, pp. 109836.
Elsevier DOI 2310
Spectral embedding, Multi-view clustering, Ensemble clustering, Graph reconstruction BibRef

Wang, R.[Rong], Chen, H.M.[Hui-Min], Lu, Y.H.[Yi-Hang], Zhang, Q.R.[Qian-Rong], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Discrete and Balanced Spectral Clustering With Scalability,
PAMI(45), No. 12, December 2023, pp. 14321-14336.
IEEE DOI 2311
BibRef

Zhou, B.[Bo], Liu, W.L.[Wen-Liang], Shen, M.[Meizhou], Lu, Z.Y.[Zheng-Yu], Zhang, W.Z.[Wen-Zhen], Zhang, L.[Luyun],
Adaptive graph fusion learning for multi-view spectral clustering,
PRL(176), 2023, pp. 102-108.
Elsevier DOI 2312
Multi-view data, Multiple kernel learning, Graph fusion, Spectral clustering BibRef


Wang, Y.Y.[Yong-Yu],
Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation,
ICPR22(3063-3068)
IEEE DOI 2212
Scalability, Clustering algorithms, Noise measurement, Time complexity, Standards, Spectral analysis BibRef

El Hajjar, S.[Sally], Dornaika, F.[Fadi], Abdallah, F.[Fahed], Omrani, H.[Hichem],
Multi-view Spectral Clustering via Integrating Label and Data Graph Learning,
CIAP22(III:109-120).
Springer DOI 2205
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Sahoo, S.[Saswata], Chakraborty, S.[Souradip],
Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type,
ICPR21(5712-5719)
IEEE DOI 2105
Laplace equations, Probabilistic logic, Numerical models BibRef

Liu, X.Y.[Xin-Yue], Yang, S.C.[Shi-Chong], Zong, L.L.[Lin-Lin],
Constrained Spectral Clustering Network with Self-Training,
ICPR21(2861-2866)
IEEE DOI 2105
Clustering methods, Clustering algorithms, Benchmark testing BibRef

Piao, X.L.[Xing-Lin], Hu, Y.L.[Yong-Li], Gao, J.B.[Jun-Bin], Sun, Y.F.[Yan-Feng], Yang, X.[Xin], Yin, B.C.[Bao-Cai],
Reweighted Non-Convex Non-Smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold,
ACCV20(V:562-577).
Springer DOI 2103
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Mouden, Z.A.E., Jakimi, A.,
k-eNSC: k-estimation for Normalized Spectral Clustering,
ISCV20(1-5)
IEEE DOI 2011
pattern clustering, spectral analysis, unsupervised learning, normalized spectral clustering, dynamic estimation, BibRef

Muzeau, J.[Julien], Oliver-Parera, M.[Maria], Ladret, P.[Patricia], Bertolino, P.[Pascal],
Combining Mixture Models and Spectral Clustering for Data Partitioning,
ICIAR20(II:63-75).
Springer DOI 2007
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Yang, X.[Xu], Deng, C.[Cheng], Zheng, F.[Feng], Yan, J.C.[Jun-Chi], Liu, W.[Wei],
Deep Spectral Clustering Using Dual Autoencoder Network,
CVPR19(4061-4070).
IEEE DOI 2002
BibRef

Huang, S., Zhang, L., Li, F.,
Spectral Embedded Clustering on Multi-Manifold,
ICPR18(391-396)
IEEE DOI 1812
Manifolds, Complexity theory, Clustering algorithms, Linearity, Laplace equations, Probabilistic logic, Matrices, multi-manifold BibRef

Chen, G.L.[Guang-Liang],
A Scalable Spectral Clustering Algorithm Based on Landmark-Embedding and Cosine Similarity,
SSSPR18(52-62).
Springer DOI 1810
BibRef

Challa, A.[Aditya], Danda, S.[Sravan], Sagar, B.S.D.[B. S. Daya], Najman, L.[Laurent],
An Introduction to Gamma-Convergence for Spectral Clustering,
DGCI17(185-196).
Springer DOI 1711
BibRef

Løkse, S.[Sigurd], Bianchi, F.M.[Filippo M.], Salberg, A.B.[Arnt-Børre], Jenssen, R.[Robert],
Spectral Clustering Using PCKID: A Probabilistic Cluster Kernel for Incomplete Data,
SCIA17(I: 431-442).
Springer DOI 1706
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Banijamali, E.[Ershad], Ghodsi, A.[Ali],
Fast Spectral Clustering Using Autoencoders and Landmarks,
ICIAR17(380-388).
Springer DOI 1706
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Rodriguez, M.[Mario], Medrano, C.[Carlos], Herrero, E.[Elias], Orrite, C.[Carlos],
Spectral Clustering Using Friendship Path Similarity,
IbPRIA15(319-326).
Springer DOI 1506
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Chakeri, A., Farhidzadeh, H., Hall, L.O.,
Spectral sparsification in spectral clustering,
ICPR16(2301-2306)
IEEE DOI 1705
Approximation algorithms, Clustering algorithms, Eigenvalues and eigenfunctions, Laplace equations, Resistance, Sparse matrices, Symmetric, matrices BibRef

Bruneau, P.[Pierrick], Parisot, O.[Olivier], Otjacques, B.[Benoit],
A Heuristic for the Automatic Parametrization of the Spectral Clustering Algorithm,
ICPR14(1313-1318)
IEEE DOI 1412
Clustering algorithms BibRef

Ghafarianzadeh, M.[Mahsa], Blaschko, M.B.[Matthew B.], Sibley, G.[Gabe],
Unsupervised Spatio-Temporal Segmentation with Sparse Spectral-Clustering,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Feng, J.S.[Jia-Shi], Lin, Z.C.[Zhou-Chen], Xu, H.[Huan], Yan, S.C.[Shui-Cheng],
Robust Subspace Segmentation with Block-Diagonal Prior,
CVPR14(3818-3825)
IEEE DOI 1409
BibRef

Hu, H.[Han], Zhou, J.H.[Jia-Huan], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
Multi-way constrained spectral clustering by nonnegative restriction,
ICPR12(1550-1553).
WWW Link. 1302
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Ghoshdastidar, D.[Debarghya], Dukkipati, A.[Ambedkar], Adsul, A.P.[Ajay P.], Vijayan, A.S.[Aparna S.],
Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions,
CVPR14(1472-1477)
IEEE DOI 1409
Jensen-type divergence; Kernels; Spectral Clustering; Tensor flattening BibRef

Fu, X.P.[Xi-Ping], Martin, S., Mills, S., McCane, B.,
Improved Spectral Clustering Using Adaptive Mahalanobis Distance,
ACPR13(171-175)
IEEE DOI 1408
data handling BibRef

Imbajoa-Ruiz, D.E., Gustin, I.D., Bolaños-Ledezma, M., Arciniegas-Mejía, A.F., Guasmayan-Guasmayan, F.A., Bravo-Montenegro, M.J., Castro-Ospina, A.E., Peluffo-Ordóñez, D.H.[Diego Hernán],
Multi-labeler Classification Using Kernel Representations and Mixture of Classifiers,
CIARP16(343-351).
Springer DOI 1703
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Peluffo-Ordóñez, D.H.[Diego Hernán], García-Vega, S.[Sergio], Álvarez-Meza, A.M.[Andrés Marino],
Kernel Spectral Clustering for Dynamic Data,
CIARP13(I:238-245).
Springer DOI 1311
BibRef

Peluffo-Ordóñez, D.H.[Diego Hernán], Castro-Hoyos, C., Acosta-Medina, C.D.[Carlos Daniel], Castellanos-Domínguez, C.G.[César Germán],
Quadratic Problem Formulation with Linear Constraints for Normalized Cut Clustering,
CIARP14(408-415).
Springer DOI 1411
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Earlier: A1, A3, A4, Only:
An Improved Multi-class Spectral Clustering Based on Normalized Cuts,
CIARP12(130-137).
Springer DOI 1209
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Álvarez-Meza, A.M.[Andrés Marino], Castro-Ospina, A.E.[Andrés Eduardo], Castellanos-Domínguez, C.G.[César Germán],
Spectral Clustering Using Compactly Supported Graph Building,
CIARP14(327-334).
Springer DOI 1411
BibRef
Earlier: A2, A1, Only:
Automatic Graph Building Approach for Spectral Clustering,
CIARP13(I:190-197).
Springer DOI 1311
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Mahmood, A.[Arif], Mian, A.S.[Ajmal S.], Owens, R.[Robyn],
Semi-supervised Spectral Clustering for Image Set Classification,
CVPR14(121-128)
IEEE DOI 1409
Eigen solvers BibRef

Mahmood, A.[Arif], Mian, A.S.[Ajmal S.],
Hierarchical Sparse Spectral Clustering For Image Set Classification,
BMVC12(51).
DOI Link 1301
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Huang, H.C.[Hsin-Chien], Chuang, Y.Y.[Yung-Yu], Chen, C.S.[Chu-Song],
Affinity aggregation for spectral clustering,
CVPR12(773-780).
IEEE DOI 1208
BibRef

Gao, H.D.[Hai-Dong], Zhuang, Y.T.[Yue-Ting], Wu, F.[Fei], Shao, J.[Jian],
Inverse-degree Sampling for Spectral Clustering,
ICIG11(362-367).
IEEE DOI 1109
BibRef

Li, M.[Mu], Lian, X.C.[Xiao-Chen], Kwok, J.T.[James T.], Lu, B.L.[Bao-Liang],
Time and space efficient spectral clustering via column sampling,
CVPR11(2297-2304).
IEEE DOI 1106
Eigen decomposition is cubic time, quadratic space. Use a subset of the columns. BibRef

Zhu, X.T.[Xia-Tian], Loy, C.C.[Chen Change], Gong, S.G.[Shao-Gang],
Constructing Robust Affinity Graphs for Spectral Clustering,
CVPR14(1450-1457)
IEEE DOI 1409
Robust affinity graphs BibRef

Lu, Z.W.[Zhi-Wu], Ip, H.H.S.[Horace H. S.],
Constrained Spectral Clustering via Exhaustive and Efficient Constraint Propagation,
ECCV10(VI: 1-14).
Springer DOI 1009
Constraint propogation. BibRef

Lu, Z.D.[Zheng-Dong], Carreira-Perpinan, M.A.[Miguel A.],
Constrained spectral clustering through affinity propagation,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Li, X.[Xi], Zhang, Z.F.[Zhong-Fei], Wang, Y.G.[Yan-Guo], Hu, W.M.[Wei-Ming],
Multiclass spectral clustering based on discriminant analysis,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Aiello, M., Andreozzi, F., Catanzariti, E., Isgro, F., Santoro, M.,
Fast convergence for spectral clustering,
CIAP07(641-646).
IEEE DOI 0709
Cluster using first few eigen vectors. BibRef

Li, Z.G.[Zhen-Guo], Liu, J.Z.[Jian-Zhuang], Chen, S.F.[Shi-Feng], Tang, X.[Xiaoou],
Noise Robust Spectral Clustering,
ICCV07(1-8).
IEEE DOI 0710
Regularize, the k-means. BibRef

Luo, B.[Bin], Chen, S.B.[Si-Bao],
LPP and LPP Mixtures for Graph Spectral Clustering,
PSIVT06(118-127).
Springer DOI 0612
BibRef

Yu, S.X., Shi, J.B.[Jian-Bo],
Multiclass spectral clustering,
ICCV03(313-319).
IEEE DOI 0311
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
PCA, Principal Component Analysis, Data Dimensionality Reduction .


Last update:Mar 16, 2024 at 20:36:19