Multiple kernel learning from noisy labels by stochastic programming

Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

We study the problem of multiple kernel learning from noisy labels. This is in contrast to most of the previous studies on multiple kernel learning that mainly focus on developing efficient algorithms and assume perfectly labeled training examples. Directly applying the existing multiple kernel learning algorithms to noisily labeled examples often leads to suboptimal performance due to the incorrect class assignments. We address this challenge by casting multiple kernel learning from noisy labels into a stochastic programming problem, and presenting a minimax formulation. We develop an efficient algorithm for solving the related convex-concave optimization problem with a fast convergence rate of O(I/T) where T is the number of iterations. Empirical studies on UCI data sets verify both the effectiveness and the efficiency of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages233-240
Number of pages8
Publication statusPublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume1

Other

Other29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

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All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Education

Cite this

Yang, T., Mahdavi, M., Jin, R., Zhang, L., & Zhou, Y. (2012). Multiple kernel learning from noisy labels by stochastic programming. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012 (pp. 233-240). (Proceedings of the 29th International Conference on Machine Learning, ICML 2012; Vol. 1).