PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Graph Clustering Using the Jensen-Shannon Kernel
Lu Bai and Edwin Hancock
In: Computer Analysis of Images and Patterns - 14th International Conference, CAIP 2011, August 29-31, 2011, Seville, Spain.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:8551
Deposited By:Edwin Hancock
Deposited On:13 February 2012

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