Graph clustering using the Jensen-Shannon Kernel

Authors:
Lu Bai;Edwin R. Hancock
Affiliations:
Department of Computer Science, University of York;Department of Computer Science, University of York
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

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Abstract

This paper investigates whether the Jensen-Shannon divergence can be used as a means of establishing a graph kernel for graph classification. The Jensen-Shannon kernel is nonextensive information theoretic kernel which is derived from mutual information theory, and is defined on probability distributions. We use the von-Neumann entropy to calculate the elements of the Jensen-Shannon graph kernel and use the kernel matrix for graph classification. We use kernel principle components analysis (kPCA) to embed graphs into a feature space. Experimental results reveal the method gives good classification results on graphs extracted from an object recognition database.