Information Theoretic Kernel Integration
published: Jan. 19, 2010, recorded: December 2009, views: 3558
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !