@inproceedings{0f075e7e9c024f65a3c5f8fc3a35f153,

title = "Multiple kernel learning from noisy labels by stochastic programming",

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.",

author = "Tianbao Yang and Mehrdad Mahdavi and Rong Jin and Lijun Zhang and Yang Zhou",

year = "2012",

language = "English (US)",

isbn = "9781450312851",

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

pages = "233--240",

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

note = "29th International Conference on Machine Learning, ICML 2012 ; Conference date: 26-06-2012 Through 01-07-2012",

}