Multiple kernel clustering

Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

Volumn 2, Issue , 2009, Pages 634-645

  Zhao, Bin ^a   Kwok, James T ^a   Zhang, Changshui ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER LABELING; DATA SAMPLE; FEATURE SPACE; KERNEL FUNCTION; KERNEL METHODS; LABELINGS; MACHINE LEARNING COMMUNITIES; MAXIMUM MARGIN; MULTI-CLASS; MULTIPLE KERNELS; REAL WORLD DATA; TIME COMPLEXITY;

CLUSTER ANALYSIS; GEOMETRY; LABELING; METALLIC MATRIX COMPOSITES;

CLUSTERING ALGORITHMS;

EID: 72749126111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML, 2004.
2. O. Bousquet and D. Herrmann. On the complexity of learning the kernel matrix. In NIPS, 2003.
3. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
4. P. K. Chan, D. F. Schlag, and J. Y. Zien. Spectral k-way ratio-cut partitioning and clustering. IEEE Trans. Computer-Aided Design, 13:1088-1096, 1994.
5. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46:131-159, 2002.
6. O. Chapelle and A. Zien. Semi-supervised classi cation by low density separation. In AISTATS, 2005.
7. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. JMLR, 2:265-292, 2001.
8. C. Ding, X. He, H. Zha, M. Gu, and H. D. Simon. A min-max cut algorithm for graph partitioning and data mining. In ICDM, pages 107-114, 2001.
9. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classi cation. John Wiley & Sons, Inc., 2001.
10. M. Gonen and E. Alpaydin. Localized multiple kernel learning. In ICML, 2008.
11. T. Joachims. Training linear SVMs in linear time. In SIGKDD, 2006.
12. J. E. Kelley. The cutting-plane method for solving convex programs. Journal of the Society for Industrial Applied Mathematics, 8:703-712, 1960.
13. G. Lanckriet, T. De Bie, N. Cristianini, M. Jordan, and W. Noble. A statistical framework for genomic data fusion. Bioinfomatics, 20(16):2626-2635, 2004.
14. G. Lanckriet, N. Cristianini, L. Ghaoui, P. Bartlett, and M. Jordan. Learning the kernel matrix with semide nite programming. JMLR, 5:27-72, 2004.
15. M. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret. Applications of second-order cone programming. Linear Algebra Appl., 284:193-228, 1998.
16. Y. Nesterov and A. Nemirovskii. Interior-Point Polynomial Algorithms in Convex Programming. 1994.
17. C. Ong, A. Smola, and R. Williamson. Learning the kernel with hyperkernels. JMLR, 6:1043-1071, 2005.
18. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. More efficiency in multiple kernel learning. In ICML, 2007.
19. W. Rand. Objective criteria for the evaluation of clustering methods. JASA, 66:846-850, 1971.
20. B. Schölkopf, A. J. Smola, and K. R. Müller. Kernel principal component analysis. Advances in kernel methods: Support vector learning, pages 327-352, 1999.
21. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.
22. A. J. Smola, S.V.N. Vishwanathan, and T. Hofmann. Kernel methods for missing variables. In AISTATS, 2005.
23. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. JMLR, 7:1531-1565, 2006.
24. P. Sun and X. Yao. Boosting kernel models for regression. In ICDM, 2006.
25. I. Tsang and J. Kwok. Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks, 17(1):48-58, 2006.
26. H. Valizadegan and R. Jin. Generalized maximum margin clustering and unsupervised kernel learning. In NIPS, 2007.
27. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum margin clustering. In NIPS, 2004.
28. L. Xu and D. Schuurmans. Unsupervised and semi-supervised multi-class support vector machines. In AAAI, 2005.
29. J. Ye, S. Ji, and J. Chen. Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming. In SIGKDD, 2007.
30. K. Zhang, I. W. Tsang, and J. T. Kwok. Maximum margin clustering made practical. In ICML, 2007.
31. B. Zhao, F. Wang, and C. Zhang. Efficient maximum margin clustering via cutting plane algorithm. In SDM, 2008.
32. B. Zhao, F. Wang, and C. Zhang. Efficient multiclass maximum margin clustering. In ICML, 2008.
33. A. Zien and C. Ong. Multiclass multiple kernel learning. In ICML, 2007.