Information Theoretic Kernel Integration
published: Jan. 19, 2010, recorded: December 2009, views: 3557
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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.
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