Two-stage multiple kernel learning with multiclass kernel polarization

The success of kernel methods is very much dependent on the choice of kernels. Multiple kernel learning (MKL) aims at learning a combination of different kernels in order to better match the underlying problem instead of using a single fixed kernel. In this paper, we propose a simple but effective multiclass MKL method by a two-stage strategy, in which the first stage finds the kernel weights to combine the kernels, and the second stage trains a standard multiclass support vector machine (SVM). Specifically, we first present an evaluation criterion named multiclass kernel polarization (MKP) to assess the quality of a kernel in the multiclass classification scenario, and then develop a heuristic rule to directly assign a weight to each kernel based on the quality of the individual kernel. MKP is a multiclass extension of the kernel polarization, which is a universal kernel evaluation criterion for kernel design and learning. Comprehensive experiments are conducted on several UCI benchmark examples and the results well demonstrate the effectiveness and efficiency of our approach.