An Online Kernel Selection Wrapper via Multi-Armed Bandit Model

Online kernel selection is critical to online kernel learning, but most of the existing online kernel learning methods ignore the online kernel selection process, and instead they empirically preset and fix a kernel or adjust kernel parameters by gradient descent, which is sensitive to the initial setting and has no theoretical guarantee. In this work, we propose an online kernel selection wrapper via the multi-armed bandit model, which can select a kernel at each round from a set of candidate kernels with theoretical guarantee and can be applied to any online kernel learning model. Specifically, the wrapper consists of two layers. In the outer layer, the wrapper corresponds each candidate kernel to an arm of the multi-armed bandit model, and chooses an arm according to the probability distribution maintained by the model at each round. In the inner layer, the wrapper updates the probability distribution according the loss of the selected arm, which is incurred by the prediction of the online kernel learning algorithm. We propose a new online kernel selection regret to measure the performance of the proposed wrapper, and prove that the proposed wrapper enjoys a sub-linear expected online kernel selection regret with respect to the cumulative loss of the optimal kernel among the candidates kernels. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed wrapper.