14.1.3.5.1 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.
WWW Link. 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.
WWW Link. 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

Ozertem, U.[Umut], Erdogmus, D.[Deniz], Jenssen, R.[Robert],
Mean shift spectral clustering,
PR(41), No. 6, June 2008, pp. 1924-1938.
WWW Link. 0802
Similarity based clustering; Nonparametric density estimation; Mean shift; Connected components; Spectral clustering BibRef

Zhang, X.C.[Xian-Chao], Li, J.W.[Jing-Wei], Yu, H.[Hong],
Local density adaptive similarity measurement for spectral clustering,
PRL(32), No. 2, 15 January 2011, pp. 352-358.
Elsevier DOI 1101
Clustering; Spectral clustering; Similarity measure 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.
WWW Link. 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

Zhang, Q.[Qian], Tian, Y.[Yuan], Yang, Y.[Yiping], Pan, C.H.[Chun-Hong],
Automatic Spatial-Spectral Feature Selection for Hyperspectral Image via Discriminative Sparse Multimodal Learning,
GeoRS(53), No. 1, January 2015, pp. 261-279.
IEEE DOI 1410
feature selection 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

Zhang, H., Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Li, P.,
Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images,
GeoRS(54), No. 6, June 2016, pp. 3672-3684.
IEEE DOI 1606
geophysical image processing BibRef

Zhai, H.[Han], Zhang, H.Y.[Hong-Yan], Xu, X.[Xiong], Zhang, L.P.[Liang-Pei], Li, P.X.[Ping-Xiang],
Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Zhang, H.Y.[Hong-Yan], Zhai, H.[Han], Liao, W.[Wenzhi], Cao, L.[Liqin], Zhang, L.P.[Liang-Pei], Pižurica, A.[Aleksandra],
Hyperspectral Image Kernel Sparse Subspace Clustering With Spatial Max Pooling Operation,
ISPRS16(B3: 945-948).
DOI Link 1610
BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification,
PandRS(94), No. 1, 2014, pp. 25-36.
Elsevier DOI 1407
Kernel method BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Huang, X., Zhang, L.,
Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification,
GeoRS(52), No. 9, Sept 2014, pp. 5923-5936.
IEEE DOI 1407
Dictionaries BibRef

Zhang, Y.X.[Yu-Xiang], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Liu, T.L.[Tong-Liang],
Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection,
GeoRS(55), No. 2, February 2017, pp. 894-906.
IEEE DOI 1702
geophysical image processing BibRef

Zhang, Y.X.[Yu-Xiang], Wu, K.[Ke], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Huang, Y., Zhang, L.P.[Liang-Pei],
Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation With a Locally Adaptive Dictionary,
GeoRS(52), No. 6, June 2014, pp. 3707-3719.
IEEE DOI 1403
Collaboration BibRef

Zhang, L.P.[Liang-Pei], Huang, X., Huang, B.[Bo], Li, P.X.[Ping-Xiang],
A Pixel Shape Index Coupled With Spectral Information for Classification of High Spatial Resolution Remotely Sensed Imagery,
GeoRS(44), No. 10, October 2006, pp. 2950-2961.
IEEE DOI 0609
BibRef

Li, J.Y.[Jia-Yi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Efficient Superpixel-Level Multitask Joint Sparse Representation for Hyperspectral Image Classification,
GeoRS(53), No. 10, October 2015, pp. 5338-5351.
IEEE DOI 1509
computational complexity BibRef

Xia, G.S.[Gui-Song], Wang, Z.F.[Zi-Feng], Xiong, C.M.[Cai-Ming], Zhang, L.P.[Liang-Pei],
Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge,
RS(7), No. 11, 2015, pp. 15014.
DOI Link 1512
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.[Qilin], 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

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

Wang, Y.Q.[Yue-Qing], Liu, X.W.[Xin-Wang], Dou, Y.[Yong], Lv, Q.[Qi], Lu, Y.[Yao],
Multiple kernel learning with hybrid kernel alignment maximization,
PR(70), No. 1, 2017, pp. 104-111.
Elsevier DOI 1706
Multiple kernel learning 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


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
BibRef

Banijamali, E.[Ershad], Ghodsi, A.[Ali],
Fast Spectral Clustering Using Autoencoders and Landmarks,
ICIAR17(380-388).
Springer DOI 1706
BibRef

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

Orrite, C.[Carlos], Rodriguez, M.[Mario], Medrano, C.[Carlos],
One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process,
ICPR16(2694-2699)
IEEE DOI 1705
Computational modeling, Encoding, Feature extraction, Hidden Markov models, Kernel, Videos BibRef

Rodriguez, M.[Mario], Medrano, C.[Carlos], Herrero, E.[Elias], Orrite, C.[Carlos],
Spectral Clustering Using Friendship Path Similarity,
IbPRIA15(319-326).
Springer DOI 1506
BibRef

Vo, P.D.[Phong D.], Sahbi, H.[Hichem],
Contextual kernel map learning for scene transduction,
ICIP15(3797-3801)
IEEE DOI 1512
BibRef
Earlier:
Modeling label dependencies in kernel learning for image annotation,
ICIP14(5886-5890)
IEEE DOI 1502
BibRef
Earlier:
Transductive inference and kernel design for object class segmentation,
ICIP12(2173-2176).
IEEE DOI 1302
context; kernel map learning; scene understanding; transductive inference. Kernel 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.[Jiashi], 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
BibRef

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
BibRef

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
BibRef
Earlier: A1, A3, A4, Only:
An Improved Multi-class Spectral Clustering Based on Normalized Cuts,
CIARP12(130-137).
Springer DOI 1209
BibRef

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
BibRef

Uzair, M.[Muhammad], Mahmood, A.[Arif], Mian, A.S.[Ajmal S.],
Sparse Kernel Learning for Image Set Classification,
ACCV14(II: 617-631).
Springer DOI 1504
BibRef

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
BibRef

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:Nov 18, 2017 at 20:56:18