Marginalized Kernels between Labeled Graphs

Kashima, H; Tsuda, K; Inokuchi, A

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Marginalized Kernels between Labeled Graphs

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Tsuda, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized Kernels between Labeled Graphs. In T. Fawcett, & N. Mishra (Eds.), Twentieth International Conference on Machine Learning (ICML 2003) (pp. 321-328). Menlo Park, CA, USA,: AAAI Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DBFE-3

Abstract

A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discrete-time linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds.