Multiple Kernel Learning from Noisy Labels by Stochastic Programming
This work addresses noisy label handling in multiple kernel learning, an incremental improvement for machine learning practitioners dealing with imperfect data.
The paper tackles the problem of multiple kernel learning with noisy labels, which causes suboptimal performance in existing methods, by formulating it as a stochastic programming problem and developing an efficient algorithm with a convergence rate of O(1/T). Empirical results on UCI datasets confirm the framework's effectiveness and algorithm efficiency.
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 of the proposed framework and the efficiency of the proposed optimization algorithm.