Clustering via kernel decomposition

IEEE Transactions on Neural Networks

Volumn 17, Issue 1, 2006, Pages 256-264

  Szymkowiak Have, A ^a   Girolami, M A ^b   Larsen, Jan ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Aggregated Markov model; Kernel decomposition; Kernel principal component analysis (KPCA); Spectral clustering

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; MARKOV PROCESSES; MATRIX ALGEBRA; PROBABILITY; SPECTRUM ANALYSIS;

AFFINITY MATRIX; AGGREGATED MARKOV MODEL; EIGENVALUE DECOMPOSITION; KERNEL DECOMPOSITION; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); NONPARAMETRIC DENSITY ESTIMATOR; POSTERIOR PROBABILITIES; SPECTRAL CLUSTERING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 33144465910     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2005.860840     Document Type: Article

References (18)

1. A. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Advances in Neural Information Processing Systems 14, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds. Cambridge, MA: MIT Press, 2002, pp. 849-856.
2. F. R. Bach and M. I. Jordan, "Learning spectral clustering," in Advances in Neural Information Processing Systems, 2003.
3. R. Kannan, S. Vempala, and A. Vetta. (2000) "On clusterings: Good, bad and spectral," Tech. Rep.. Yale Univ., CS Dep.. [Online]. Available: citeseer.nj.nec.com/495 691.html
4. B. Scholkopf, A. Smola, and K.-R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, pp. 1299-1319, 1998.
5. C. William and M. Seeger, "Using the Nystrom method to speed up kernel machines," in Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp, Eds. Cambridge, MA: MIT Press, 2000, vol. 13, pp. 682-688.
6. M. Girolami, "Orthogonal series density estimation and the kernel eigen-value problem," Neural Comput., vol. 14, no. 3, pp. 669-688, 2002.
7. Y. Bengio, P. Vincent, and J.-F. Paiement, ""Learning eigenfuncions of similarity: Linking spectral clustering and kernel PCA," Tech. Rep.," Univ. Montréal, Dép. d'informatique et recherche opérationnelle, 2003.
8. A. J. Izenman, "Recent developments in nonparametric density estimation," J. Amer. Statist. Assoc., vol. 86, pp. 205-224, 1991.
9. B. Silverman, "Density estimation for statistics and data analysis," Monographs on Statist. Appl. Probabil., 1986.
10. D. F. Specht, "A general regression neural network," IEEE Trans. Neural. Netw., vol. 2, pp. 568-576, 1991.
11. L. Saul and F. Pereira, "Aggregate and mixed-order Markov models for statistical language processing," in Proc. 2nd Conf. Empirical Methods in Natural Language Processing, C. Cardie and R. Weischedel, Eds., Somerset, NJ, 1997, pp. 81-89.
12. D. D. Lee and H. S. Seung, "Algorithms for nonnegative matrix factorization," in Advances in Neural Information Processing Systems 13, T. K. Leen, T. G. Dietterich, and V. Tresp, Eds. Cambridge, MA: MIT Press, 2001, pp. 556-562.
13. D. Donoho and V. Stodden, "When does nonnegative matrix factorization give a correct decomposition into parts," in Advances in Neural Information Processing Systems, 2003.
14. T. Hofmann, "Probabilistic latent semantic indexing," in Proc. 22nd Ann. ACM Conf. Res. Develop. Information Retrieval, Berkeley, CA, Aug. 1999, pp. 50-57.
15. M. Meila and J. Shi, "Learning segmentation by random walks," in Advances in Neural Information Processing Systems 13, T. K. Leen, T. G. Dietterich, and V. Tresp, Eds. Cambridge, MA: MIT Press, 2001, pp. 873-879.
16. J. Larsen, L. Hansen, A. Szymkowiak-Have, T. Christiansen, and T. Kolenda, "Webmining: Learning from the world wide web," Special Issue of Computat. Statist. Data Anal., vol. 38, pp. 517-532, 2002.
17. J. Larsen, A. Szymkowiak-Have, and L. Hansen, "Probabilistic hierarchical clustering with labeled and unlabeled data," Int. J. Knowledge-Based Intell. Eng. Syst., vol. 6, no. 1, pp. 56-62, 2002.
18. A. Szymkowiak, J. Larsen, and L. Hansen, "Hierarchical clustering for datamining," in Proc. KES-2001 5th Int. Conf. Knowledge-Based Intelligent Info. Eng. Syst. Allied Technol., 2001, pp. 261-265.