Latent tree models for rounding in spectral clustering

Neurocomputing

Volumn 144, Issue , 2014, Pages 448-462

  Liu, April H ^a   Poon, Leonard K M ^b   Liu, Teng Fei ^a   Zhang, Nevin L ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b THE EDUCATION UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Latent tree models; Rounding; Spectral clustering

Indexed keywords

DATA HANDLING; EIGENVALUES AND EIGENFUNCTIONS; FORESTRY;

LAPLACIAN MATRICES; NUMBER OF CLUSTERS; ROUNDING; ROUNDING METHOD; SIMILARITY MATRIX; SPECTRAL CLUSTERING; SUB-PROBLEMS; TREE MODELS;

MATRIX ALGEBRA;

ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; EIGENVECTOR; INFORMATION PROCESSING; LAPLACIAN MATRIX; LATENT TREE MODEL; PRIORITY JOURNAL; SPECTRAL CLUSTERING; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS;

DATA PROCESSING; FORESTRY; MATHEMATICAL MODELS;

EID: 84906056065     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.030     Document Type: Article

References (22)

1. L.K.M. Poon, A.H. Liu, T. Liu, N.L. Zhang, A Model-based approach to rounding in spectral clustering, in: Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, 2012, pp. 685-694.
2. von Luxburg U. A tutorial on spectral clustering. Stat. Comput. 2007, 17:395-416.
3. L. Hagen, A.B. Kahng, A New approach to effective circuit clustering, in: Proceedings of the IEEE International Conference on Computer Aided Design, 1992, pp. 422-427.
4. J. Shi, J. Malik, Normalized cuts and image segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 731-737.
5. Rendl F., Wolkowicz H. A projection technique for partitioning the nodes of a graph. Ann. Operat. Res. 1995, 58(3):155-179.
6. Bach F.R., Jordan M.I. Learning spectral clustering, with application to speech separation. J. Mach. Learn. Res. 2006, 7:1963-2001.
7. Zhang Z., Jordan M.I. Multiway spectral clustering. a margin-based perspective. Stat. Sci. 2008, 23(3):383-403.
8. Rebagliati N., Verri A. Spectral clustering with more than K eigenvectors. Neurocomputing 2011, 74:1391-1401.
9. L. Zelnik-Manor, P. Perona, Self-tuning spectral clustering, in: Advances in Neural Information Processing Systems, 2005.
10. Xiang T., Gong S. Spectral clustering with eigenvector selection. Pattern Recognit. 2008, 41(3):1012-1029.
11. Zhao F., Jiao L., Liu H., Gao X., Gong M. Spectral clustering with eigenvector selection based on entropy ranking. Neurocomputing 2010, 73:1704-1717.
12. R. Socher, A. Maas, C.D. Manning, Spectral Chinese restaurant processes: nonparametric clustering based on similarities, in: Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.
13. Zhang N.L. Hierarchical latent class models for cluster analysis. J. Mach. Learn. Res. 2004, 5:697-723.
14. A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clusterings: analysis and an algorithm, in: Advances in Neural Information Processing Systems, vol. 14, 2002.
15. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 1977, 39(1):1-38.
16. Schwarz G. Estimating the dimension of a model. Ann. Stat. 1978, 6(2):461-464.
17. Rand W.M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 1971, 66(336):846-850.
18. Meilǎ M. Comparing clusterings-an information based distance. J. Multivar. Anal. 2007, 98:873-895.
19. Chen T., Zhang N.L., Liu T., Poon K.M., Wang Y. Model-based multidimensional clustering of categorical data. Artif. Intell. 2012, 176:2246-2269.
20. C. Fraley, A.E. Raftery, MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report 504, Department of Statistics, University of Washington (revised 2009), 2006.
21. Hinton G.E., Salakhutdinov R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313(5786):504-507.
22. Arbeláez P., Maire M., Fowlkes C., Malik J. Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(5):898-916.