Abstract

In this paper we explore how to construct a Jensen-Shannon kernel for hypergraphs. We commence by calculating probability distribution over the steady state random walk on a hypergraph. The Shannon entropies required to construct the Jensen-Shannon divergence for pairs of hypergraphs are obtained from steady state probability distributions of the random walk. The Jensen-Shannon divergence between a pair of hypergraphs is the difference between the Shannon entropies of the separate hypergraphs and a composite structure. Our proposed kernel is not restricted to hypergraphs. Experiments on (hyper)graph datasets extracted from bioinformatics and computer vision datasets demonstrate the effectiveness and efficiency of the Jensen-Shannon hypergraph kernel for classification and clustering.

Keywords

Disjoint Union Shannon Entropy Incidence Matrix Edit Operation Minimum Vertex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lu Bai
    • 1
  • Edwin R. Hancock
    • 1
  • Peng Ren
    • 2
  1. 1.Department of Computer ScienceUniversity of York, UKHeslingtonUK
  2. 2.College of Information and Control EngineeringChina University of Petroleum (Huadong)China

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