Unsupervised Robust Multiple Kernel Learning via Extracting Local and Global Noises

Kernel-based clustering methods can capture the non-linear structure and identify arbitrarily shaped clusters, so they have been widely used in machine learning tasks. Since the performance of kernel methods critically depends on the choices of kernels, multiple kernel learning methods are proposed to alleviate the effort for kernel designing. The conventional multiple kernel learning methods learn a consensus kernel by linearly combining all candidate kernels, whereas ignoring the influence of the noises. To improve the robustness of multiple kernel learning methods, in this paper, we analyze the local and global noises and design regularized terms to characterize them respectively. Then, we propose a novel local and global de-noising multiple kernel learning method which can explicitly extract the local and global noises to recover the clean kernels for multiple kernel learning. After that, a block coordinate descent algorithm is presented to solve the optimization problem. Finally, the extensive experiments on benchmark data sets will demonstrate the effectiveness of the proposed algorithm.