Bilinear formulated multiple kernel learning for multi-class classification problem

Abstract

In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for the kernels. However, the multi-class problems are rarely incorporated in the formulation and the optimization is time-consuming. We formulate the multi-class MKL in a bilinear form and propose a scheme for computationally efficient optimization. The scheme makes the method favorably applicable to large-scaled samples in the real-world problems. In the experiments on multi-class classification using several datasets, the proposed method exhibits the favorable performance and low computation time compared to the previous methods. © 2010 Springer-Verlag.