  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.
