Multiple kernel learning from noisy labels by stochastic programming

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 233-240

  Yang, Tianbao ^a   Mahdavi, Mehrdad ^a   Jin, Rong ^a   Zhang, Lijun ^b   Zhou, Yang ^c  

^a Michigan State University   (United States)

^b ZHEJIANG UNIVERSITY   (China)

^c YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS ASSIGNMENTS; DATA SETS; EMPIRICAL STUDIES; FAST CONVERGENCE RATE; MINIMAX; MULTIPLE KERNEL LEARNING; NOISY LABELS; NUMBER OF ITERATIONS; OPTIMIZATION PROBLEMS; SUB-OPTIMAL PERFORMANCE; TRAINING EXAMPLE;

LEARNING SYSTEMS; STOCHASTIC PROGRAMMING;

LEARNING ALGORITHMS;

EID: 84867135321     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Bach, Francis R. Consistency of the group lasso and multiple kernel learning. JMLR, 9:1179-1225, 2008.
2. Bach, Francis R., Lanckriet, Gert R. G., and Jordan, Michael I. Multiple kernel learning, conic duality, and the smo algorithm. In ICML, 2004.
3. Ben-Tal, Aharon, Ghaoui, Laurent El, and Nemirovski, Arkadi. Robust Optimization. Princeton University Press, 2009.
4. Bhadra, Sahely, Bhattacharya, Sourangshu, Bhattacharyya, Chiranjib, and Ben-Tal, Aharon. Robust formulations for handling uncertainty in kernel matrices. In ICML, pp. 71-78, 2010.
5. Biggio, Battista, Nelson, Blaine, and Laskov, Pavel. Support vector machines under adversarial label noise. In ACML, pp. 97-112, 2011.
6. Boucheron, S., Bousquet, O., and Lugosi, G. Concentration Inequalities, pp. 208-240. Springer, 2004.
7. Cortes, Corinna, Mohri, Mehryar, and Rostamizadeh, Afshin. L2 regularization for learning kernels. In UAI, pp. 109-116, 2009.
8. Cortes, Corinna, Mohri, Mehryar, and Rostamizadeh, Afshin. Two-stage learning kernel algorithms. In ICML, pp. 239-246, 2010a.
9. Cortes, Corinna, Mohri, Mehryar, and Rostamizadeh, Afshin. Generalization bounds for learning kernels. In ICML, pp. 247-254, 2010b.
10. Cristianini, Nello, Kandola, Jaz, Elisseeff, Andre, and Shawe-Taylor, John. On kernel-target alignment. In NIPS, volume 14, pp. 367-373, 2002.
11. Dekel, Ofer and Singer, Yoram. Support vector machines on a budget. In NIPS, pp. 345-352, 2006.
12. Huang, Kaizhu, Jin, Rong, Xu, Zenglin, and Liu, Chen-Lin. Robust metric learning by smooth optimization. In UAI, 2010.
13. Kall, Peter and Wallace, Stein W. Stochastic Programming. 1994.
14. Kloft, Marius, Brefeld, Ulf, Sonnenburg, Soeren, Laskov, Pavel, Müller, Klaus-Robert, and Zien, Alexander. Efficient and accurate 1p-norm multiple kernel learning. In NIPS, pp. 997-1005, 2009.
15. Lanckriet, Gert, Cristianini, Nello, Bartlett, Peter, and Ghaoui, Laurent E. Learning the kernel matrix with semidefinite programming. JMLR, 5:27-72, 2004.
16. Lawrence, Neil D. and Schölkopf, Bernhard. Estimating a kernel fisher discriminant in the presence of label noise. In ICML, pp. 306-313, 2001.
17. Micchelli, Charles A. and Pontil, Massimiliano. Learning the kernel function via regularization. JMLR, 6:1099-1125, 2005.
18. Nemirovski, A. Efficient methods in convex programming. 1994.
19. Nemirovski, Arkadi. Prox-method with rate of convergence o(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems. SIAM Journal on Optimization, 15:229-251, 2005.
20. Nesterov, Yu. Gradient methods for minimizing composite objective function. Technical report, 2007.
21. Pal, Chris, Mann, Gideon, and Minerich, Richard. Putting semantic information extraction on the map: Noisy label models for fact extraction. In AAAI Workshop on Information Integration on the Web, 2007.
22. Rakotomamonjy, Alain, Bach, Francis R., Canu, Stéphane, and Grandvalet, Yves. SimpleMKL. JMLR, 9:2491-2521, 2008.
23. Ramakrishnan, Ganesh, Chitrapura, Krishna Prasad, Krishnapuram, Raghu, and Bhattacharyya, Pushpak. A model for handling approximate, noisy or incomplete labeling in text classification. In ICML, 2005.
24. Robbins, H. and Monro, S. A stochastic approximation method. 22:400-407, 1951.
25. Schölkopf, Bernhard and Smola, Alexander J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA, 2001. ISBN 0262194759.
26. Sonnenburg, Sören, Rätsch, Gunnar, and Schäfer, Christin. A general and efficient multiple kernel learning algorithm. In NIPS, pp. 1273-1280, 2006.
27. Tseng, Paul. On accelerated proximal gradient methods for convex-concave optimization. Technical report, 2008.
28. Xu, Linli, Crammer, Koby, and Schuurmans, Dale. Robust support vector machine training via convex outlier ablation. In AAAI, pp. 536-542, 2006.
29. Xu, Zenglin, Jin, Rong, Yang, Haiqin, King, Irwin, and Lyu, Michael R. Simple and efficient multiple kernel learning by group lasso. In ICML, pp. 1175-1182, 2010.
30. Yang, Tianbao, Jin, Rong, and Jain, Anil K. Learning from noisy side information by generalized maximum entropy model. In ICML, pp. 1199-1206, 2010.
31. Ying, Yiming and Campbell, Colin. Generalization bounds for learning the kernel problem. In COLT, 2009.
32. Yu, Yaoliang, Yang, Min, Xu, Linli, White, Martha, and Schuurmans, Dale. Relaxed clipping: A global training method for robust regression and classification. In NIPS, pp. 2532-2540, 2011.