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
Cao, J.Z.[Jiang-Zhong],
Yu, L.G.[Liang-Geng],
Ling, B.W.K.[Bingo Wing-Kuen],
Yao, Z.J.[Zi-Jie],
Dai, Q.Y.[Qing-Yun],
MHSAN: Multi-view hierarchical self-attention network for 3D shape
recognition,
PR(150), 2024, pp. 110315.
Elsevier DOI
2403
3D shape recognition, Self-attention, Multi-view learning, View aggregation
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, Z.T.[Zi-Tong],
Chen, X.J.[Xiao-Jun],
Wang, C.[Chen],
Wang, R.[Ruili],
Song, W.[Wei],
Nie, F.P.[Fei-Ping],
A Structured Bipartite Graph Learning method for ensemble clustering,
PR(160), 2025, pp. 111133.
Elsevier DOI
2501
Clustering, Ensemble clustering, Structure learning
BibRef
Gao, C.H.[Chen-Hui],
Chen, W.Z.[Wen-Zhi],
Nie, F.P.[Fei-Ping],
Yu, W.Z.[Wei-Zhong],
Wang, Z.H.[Zong-Hui],
Spectral clustering with linear embedding: A discrete clustering
method for large-scale data,
PR(151), 2024, pp. 110396.
Elsevier DOI
2404
Spectral clustering, Graph embedding, Unsupervised learning
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
Zhao, Z.[Zihua],
Cao, Z.[Zhe],
Xin, H.[Haonan],
Wang, R.[Rong],
Wu, D.Y.[Dan-Yang],
Wang, Z.[Zheng],
Nie, F.P.[Fei-Ping],
Enhancing Clustering Performance With Tensorized High-Order Bipartite
Graphs: A Structured Graph Learning Approach,
CirSysVideo(35), No. 3, March 2025, pp. 2616-2631.
IEEE DOI Code:
WWW Link.
2503
Tensors, Bipartite graph, Noise, Clustering algorithms, Turning,
Sparse matrices, Minimization, Matrix decomposition,
tensor nuclear norm
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
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
Lin, Y.X.[Yu-Xiu],
Liu, H.[Hui],
Yu, X.[Xiao],
Zhang, C.M.[Cai-Ming],
Leveraging Transformer-based autoencoders for low-rank multi-view
subspace clustering,
PR(161), 2025, pp. 111331.
Elsevier DOI
2502
Multi-view representation learning, Subspace clustering,
Transformer, Weighted schatten -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
Xie, D.Y.[De-Yan],
Gao, Q.X.[Quan-Xue],
Zhao, Y.G.[You-Gang],
Yang, F.[Fan],
Song, W.[Wei],
Consistent graph learning for multi-view spectral clustering,
PR(154), 2024, pp. 110598.
Elsevier DOI
2406
Spectral clustering, Multi-view clustering, Tensor learning, Graph learning
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
Ding, L.[Ling],
Li, C.[Chao],
Jin, D.[Di],
Ding, S.[Shifei],
Survey of spectral clustering based on graph theory,
PR(151), 2024, pp. 110366.
Elsevier DOI
2404
Survey, Spectral Clustering. Spectral clustering, Similarity graph, Graph cut,
Laplacian matrix, Eigenvector
BibRef
Wang, K.[Kangru],
Wang, L.[Lei],
Zhang, X.L.[Xiao-Lin],
Li, J.[Jiamao],
Continual Multiview Spectral Clustering via Multilevel Knowledge,
SPLetters(31), 2024, pp. 1555-1559.
IEEE DOI
2406
Task analysis, Optimization, Data models, Correlation, TV,
Clustering algorithms, Manifolds, Multiview clustering,
multilevel knowledge guidance
BibRef
Cai, H.M.[Hong-Min],
Hu, Y.[Yu],
Qi, F.[Fei],
Hu, B.[Bin],
Cheung, Y.M.[Yiu-Ming],
Deep Tensor Spectral Clustering Network via Ensemble of Multiple
Affinity Tensors,
PAMI(46), No. 7, July 2024, pp. 5080-5091.
IEEE DOI
2406
Tensors, Costs, Artificial neural networks, Benchmark testing,
Optimization, Clustering methods, Memory management, Clustering,
clustering ensemble
BibRef
Lebeau, H.[Hugo],
Chatelain, F.[Florent],
Couillet, R.[Romain],
Asymptotic Gaussian Fluctuations of Eigenvectors in Spectral
Clustering,
SPLetters(31), 2024, pp. 1920-1924.
IEEE DOI
2408
Eigenvalues and eigenfunctions, Fluctuations, Kernel, Vectors,
Predictive models, Toy manufacturing industry, Standards,
spike eigenvector
BibRef
Guo, Y.[Yongyan],
Wu, G.[Gang],
A restarted large-scale spectral clustering with self-guiding and
block diagonal representation,
PR(156), 2024, pp. 110746.
Elsevier DOI
2408
Spectral clustering, Restarting, Self-guiding,
Block diagonal representation, Kernel trick, Nyström approximation
BibRef
Cai, H.M.[Hong-Min],
Wang, Y.[Yu],
Qi, F.[Fei],
Wang, Z.Y.[Zhuo-Yao],
Cheung, Y.M.[Yiu-Ming],
Multiview Tensor Spectral Clustering via Co-Regularization,
PAMI(46), No. 10, October 2024, pp. 6795-6808.
IEEE DOI
2409
Tensors, Manifolds, Correlation, Laplace equations, Graphical models,
Eigenvalues and eigenfunctions, Distribution functions,
spectral graph
BibRef
Zheng, Q.H.[Qing-Hai],
Flexible and Parameter-Free Graph Learning for Multi-View Spectral
Clustering,
CirSysVideo(34), No. 9, September 2024, pp. 8966-8971.
IEEE DOI
2410
Optimization, Time complexity, Bipartite graph, Laplace equations,
Task analysis, Vectors, structured graph constraint
BibRef
Nie, F.P.[Fei-Ping],
Liu, C.[Chaodie],
Wang, R.[Rong],
Li, X.L.[Xue-Long],
A Novel and Effective Method to Directly Solve Spectral Clustering,
PAMI(46), No. 12, December 2024, pp. 10863-10875.
IEEE DOI
2411
Clustering algorithms, Optimization, Clustering methods,
Laplace equations, Sparse matrices, Vectors, iterative optimization
BibRef
Lin, H.[Hang],
Peng, Y.F.[Yi-Fan],
Zhang, Y.[Yubo],
Bie, L.[Lin],
Zhao, X.B.[Xi-Bin],
Gao, Y.[Yue],
Filter Pruning by High-Order Spectral Clustering,
PAMI(47), No. 4, April 2025, pp. 2402-2415.
IEEE DOI
2503
Information filters, Redundancy, Accuracy, Correlation,
Convolutional neural networks, Neural architecture search,
high-order 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
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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
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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
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],
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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],
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Yan, J.C.[Jun-Chi],
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Deep Spectral Clustering Using Dual Autoencoder Network,
CVPR19(4061-4070).
IEEE DOI
2002
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Huang, S.,
Zhang, L.,
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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
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Chen, G.L.[Guang-Liang],
A Scalable Spectral Clustering Algorithm Based on Landmark-Embedding
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SSSPR18(52-62).
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An Introduction to Gamma-Convergence for Spectral Clustering,
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1711
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Løkse, S.[Sigurd],
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Spectral Clustering Using PCKID:
A Probabilistic Cluster Kernel for Incomplete Data,
SCIA17(I: 431-442).
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1706
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Banijamali, E.[Ershad],
Ghodsi, A.[Ali],
Fast Spectral Clustering Using Autoencoders and Landmarks,
ICIAR17(380-388).
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1706
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Medrano, C.[Carlos],
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Spectral Clustering Using Friendship Path Similarity,
IbPRIA15(319-326).
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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
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Bruneau, P.[Pierrick],
Parisot, O.[Olivier],
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A Heuristic for the Automatic Parametrization of the Spectral
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ICPR14(1313-1318)
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1412
Clustering algorithms
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Ghafarianzadeh, M.[Mahsa],
Blaschko, M.B.[Matthew B.],
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Unsupervised Spatio-Temporal Segmentation with Sparse
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Feng, J.S.[Jia-Shi],
Lin, Z.C.[Zhou-Chen],
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Robust Subspace Segmentation with Block-Diagonal Prior,
CVPR14(3818-3825)
IEEE DOI
1409
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Hu, H.[Han],
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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
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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
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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.,
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Multi-labeler Classification Using Kernel Representations and Mixture
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Kernel Spectral Clustering for Dynamic Data,
CIARP13(I:238-245).
Springer DOI
1311
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Peluffo-Ordóñez, D.H.[Diego Hernán],
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Quadratic Problem Formulation with Linear Constraints for Normalized
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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],
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Spectral Clustering Using Compactly Supported Graph Building,
CIARP14(327-334).
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1411
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Earlier: A2, A1, Only:
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1311
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Mian, A.S.[Ajmal S.],
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Semi-supervised Spectral Clustering for Image Set Classification,
CVPR14(121-128)
IEEE DOI
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Eigen solvers
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Hierarchical Sparse Spectral Clustering For Image Set Classification,
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IEEE DOI
1106
Eigen decomposition is cubic time, quadratic space.
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Robust affinity graphs
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Lu, Z.D.[Zheng-Dong],
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Aiello, M.,
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Catanzariti, E.,
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Cluster using first few eigen vectors.
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Li, Z.G.[Zhen-Guo],
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ICCV07(1-8).
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0710
Regularize, the k-means.
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Luo, B.[Bin],
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LPP and LPP Mixtures for Graph Spectral Clustering,
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0612
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Yu, S.X.,
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Multiclass spectral clustering,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
PCA, Principal Component Analysis, Data Dimensionality Reduction .