Out-of-sample eigenvectors in kernel spectral clustering

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 2349-2356

  Alzate, Carlos ^a   Suykens, Johan A K ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING MODEL; DUAL MODEL; FISHER CRITERION; LEARNING SETTINGS; MODEL SELECTION; MODEL SELECTION CRITERIA; SIMULATION RESULT; SPECIAL STRUCTURE; SPECTRAL CLUSTERING;

COMPUTER SIMULATION; CONSTRAINED OPTIMIZATION; NEURAL NETWORKS;

EIGENVALUES AND EIGENFUNCTIONS;

EID: 80054768378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033522     Document Type: Conference Paper

References (20)

1. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 888-905, 2000.
2. A. Y. Ng, M. I. 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.
3. U. von Luxburg, "A tutorial on spectral clustering," Statistics and Computing, vol. 17, no. 4, pp. 395-416, 2007.
4. C. Fowlkes, S. Belongie, F. Chung, and J. Malik, "Spectral grouping using the Nyström method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 214-225, Feb. 2004.
5. C. Alzate and J. A. K. Suykens, "Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 335-347, February 2010.
6. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines. World Scientific, Singapore, 2002.
7. C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
8. F. R. K. Chung, Spectral Graph Theory. American Mathematical Society, 1997.
9. M. Fiedler, "A property of eigenvectors of nonnegative symmetric matrices and its applications to graph theory," Czech. Math. J., vol. 25, no. 100, pp. 619-633, 1975.
10. M. Gu, H. Zha, C. Ding, X. He, and H. Simon, "Spectral relaxation models and structure analysis for k-way graph clustering and biclustering," Penn. State Univ, Computer Science and Engineering.," Technical Report, 2001.
11. C. Ding and X. He, "Linearized cluster assignment via spectral ordering," in ICML '04: Proceedings of the twenty-first International Conference on Machine Learning. New York, NY, USA: ACM Press, 2004, p. 30.
12. F. R. Bach and M. I. Jordan, "Learning spectral clustering, with application to speech separation," Journal of Machine Learning Research, vol. 7, pp. 1963-2001, 2006.
13. 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. MIT Press, 2001.
14. C. Davis and W. M. Kahan, "The rotation of eigenvectors by a perturbation. III," SIAM Journal on Numerical Analysis, vol. 7, no. 1, pp. 1-46, 1970.
15. M. Meila and J. Shi, "A random walks view of spectral segmentation," in Artificial Intelligence and Statistics AISTATS, 2001.
16. T. G. Dietterich and G. Bakiri, "Solving multiclass learning problems via error-correcting output codes," Journal of Machine Learning Research, vol. 2, pp. 263-286, 1995.
17. C. K. I. Williams and M. Seeger, "Using the Nyström method to speed up kernel machines," in Advances in Neural Information Processing Systems 13. MIT Press, 2001.
18. L. Hubert and P. Arabie, "Comparing partitions," Journal of Classification, pp. 193-218, 1985.
19. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. 8th Int'l Conf. Computer Vision, vol. 2, 2001, pp. 416-423.
20. J. Puzicha, T. Hofmann, and J. Buhmann, "Non-parametric similarity measures for unsupervised texture segmentation and image retrieval," in Computer Vision and Pattern Recognition, 1997, pp. 267-272.