Information Theoretic Kernel Integration

In this paper we consider a novel information-theoretic approach to multiple kernel learning based on minimising a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix. There are two formula- tions which we refer to as MKLdiv-dc and MKLdiv-conv. We propose to solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv- conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.