Multiple Kernel Learning from Noisy Labels by Stochastic Programming

By Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou. 2012

27 citations recorded by the source corpus.

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(1/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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning kernel logistic regression in the presence of class label noise (2014, methodology): “Data set # of Examples Dimensionality # of Kernels Ionosphere 351 34 350 Heart 270 13 140 Australia 690 14 150 Table 7 Statistics of data sets used in the comparison between the MKL algorithms rMKLR (our method) and St-PMKL [46].”
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels (2017, background): “To our knowledge, Rank Pruning is the only time-efficient solution for the open problem (Liu & Tao, 2016; Yang et al., 2012) of noise estimation.”
Regularization via Mass Transportation (2017, unknown): “[78].”
Ieee Transactions on Pattern Analysis and Machine Intelligence Classification with Noisy Labels by Importance Reweighting (unknown): “To the best of our knowledge, no efficient method has been proposed to estimate the noise rates and how to estimate them remains an open problem [6].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, methodology): “Literature Model-protected Strategy Adversarial Setting Year Techniques Goals [4] cross-models data-driven cross-attacks UnT 2010 [33] logistic regression data-driven DM UnT 2014 [72] cross-models data-driven LM UnT 2015 [99] cross-models data-driven LM UnT 2018 [98] cross-models data-driven cross-a”
On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent (2016, methodology): “To generate the datasets with noisy labels, we follow the settings in [26,19].”