Kernel-based weighted multi-view clustering

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 675-684

  Tzortzis, Grigorios ^a   Likas, Aristidis ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Kernel means; Multi view clustering; Multiple kernel learning

Indexed keywords

CLUSTERING ACCURACY; CLUSTERING PROCESS; INTRA-CLUSTER; ITERATIVE ALGORITHM; KERNEL MATRICES; MULTI-VIEW CLUSTERING; MULTI-VIEWS; MULTIPLE KERNEL LEARNING; MULTIPLE REPRESENTATION; NEGATIVE INFLUENCE; RE-COMPUTING;

CLUSTERING ALGORITHMS; DATA MINING;

MEASUREMENT ERRORS;

EID: 84874046471     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.43     Document Type: Conference Paper

References (26)

1. S. Dasgupta, M. L. Littman, and D. A. McAllester, "PAC generalization bounds for co-training," in Adv. Neural Inf. Process. Syst. 14, 2001, pp. 375-382.
2. K. Nigam and R. Ghani, "Analyzing the effectiveness and applicability of co-training," in Proc. 9th Int. Conf. Inf. Knowl. Manage., 2000, pp. 86-93.
3. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. 11th Annu. Conf. Comput. Learn. Theory, 1998, pp. 92-100.
4. S. Bickel and T. Scheffer, "Multi-view clustering," in Proc. 4th IEEE Int. Conf. Data Mining, 2004, pp. 19-26.
5. B. Long, P. S. Yu, and Z. M. Zhang, "A general model for multiple view unsupervised learning," in Proc. 8th SIAM Int. Conf. Data Mining, 2008, pp. 822-833.
6. M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, "A survey of kernel and spectral methods for clustering," Pattern Recognit., vol. 41, no. 1, pp. 176-190, 2008.
7. B. Schölkopf, A. J. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, 1998.
8. G. Tzortzis and A. Likas, "The global kernel k-means algorithm for clustering in feature space," IEEE Trans. Neural Netw., vol. 20, no. 7, pp. 1181-1194, 2009.
9. I. S. Dhillon, Y. Guan, and B. Kulis, "Weighted graph cuts without eigenvectors a multilevel approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 11, pp. 1944-1957, 2007.
10. T. Lange and J. M. Buhmann, "Fusion of similarity data in clustering," in Adv. Neural Inf. Process. Syst. 18, 2005, pp. 723-730.
11. H. Valizadegan and R. Jin, "Generalized maximum margin clustering and unsupervised kernel learning," in Adv. Neural Inf. Process. Syst. 19, 2006, pp. 1417-1424.
12. H. Zeng and Y. ming Cheung, "Kernel learning for local learning based clustering," in Proc. Int. Conf. Artif. Neural Netw. Part I, 2009, pp. 10-19.
13. B. Zhao, J. T. Kwok, and C. Zhang, "Multiple kernel clustering," in Proc. 9th SIAM Int. Conf. Data Mining, 2009, pp. 638-649.
14. S. Bickel and T. Scheffer, "Estimation of mixture models using co-em," in Proc. 16th Eur. Conf. Mach. Learn., 2005, pp. 35-46.
15. V. R. de Sa, "Spectral clustering with two views," in Proc. 22nd Int. Conf. Mach. Learn. Workshop Learn. Multiple Views, 2005, pp. 20-27.
16. D. Zhou and C. J. C. Burges, "Spectral clustering and transductive learning with multiple views," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 1159-1166.
17. M. B. Blaschko and C. H. Lampert, "Correlational spectral clustering," in Proc. 21st IEEE Conf. Comput. Vis. Pattern Recognit., 2008, pp. 1-8.
18. K. Chaudhuri, S. M. Kakade, K. Livescu, and K. Sridharan, "Multi-view clustering via canonical correlation analysis," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 129-136.
19. G. Tzortzis and A. Likas, "Multiple view clustering using a weighted combination of exemplar-based mixture models," IEEE Trans. Neural Netw., vol. 21, no. 12, pp. 1925-1938, 2010.
20. A. Kumar and H. Daumé III, "A co-training approach for multiview spectral clustering," in Proc. 28th Int. Conf. Mach. Learn., 2011, pp. 393-400.
21. D. Greene and P. Cunningham, "A matrix factorization approach for integrating multiple data views," in Proc. Eur. Conf. Mach. Learn. Knowl. Discovery Databases, 2009, pp. 423-438.
22. M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, and A. Zien, "Efficient and accurate lp-norm multiple kernel learning," in Adv. Neural Inf. Process. Syst. 22, 2009, pp. 997-1005.
23. C. Cortes, M. Mohri, and A. Rostamizadeh, "Learning nonlinear combinations of kernels," in Adv. Neural Inf. Process. Syst. 22, 2009, pp. 396-404.
24. A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Adv. Neural Inf. Process. Syst. 14, 2001, pp. 849-856.
25. Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu, "Simple and efficient multiple kernel learning by group lasso," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 1175-1182.
26. D. Lashkari and P. Golland, "Convex clustering with exemplar-based models," in Adv. Neural Inf. Process. Syst. 20, 2008, pp. 825-832.