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Graph Clustering Using the Jensen-Shannon Kernel

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Computer Analysis of Images and Patterns (CAIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6854))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, UK

    Lu Bai & Edwin R. Hancock

Authors
  1. Lu Bai
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  2. Edwin R. Hancock
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Editor information

Editors and Affiliations

  1. Dpto. Matematica Aplicada I, Escuaela Técnica Superior de Ingeniería Informática, Universite de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Pedro Real

  2. Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, University of Seville, Avenida Reina Mercedes s/n, 41012, Sevilla, Spain

    Daniel Diaz-Pernil  & Helena Molina-Abril  & 

  3. Departamento de Didáctica de la Mathemática y de las CC.Experimentales, Universidad del País Vasco-Esukal Herriko Unibertsitatea, Escuela Universitaria de Magisterio, Ramón y Cajal, 72, 48014, Bilbao (Bizcaia), Spain

    Ainhoa Berciano

  4. Institute of Computer Graphics and Algorithms, Pattern Recognition and Image Processing Group, Vienna University of Technology, Favoritenstraße 9/186-3, 1040, Vienna, Austria

    Walter Kropatsch

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© 2011 Springer-Verlag Berlin Heidelberg

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Bai, L., Hancock, E.R. (2011). Graph Clustering Using the Jensen-Shannon Kernel. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_48

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  • DOI: https://doi.org/10.1007/978-3-642-23672-3_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23671-6

  • Online ISBN: 978-3-642-23672-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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