Generating Explicit Self-Organizing Maps by Information Maximization

  • Ryotaro Kamimura
  • Haruhiko Takeuchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)


In this paper, we propose a new information theoretic method for self-organizing maps. In realizing competition, neither the winner-all-take algorithm nor lateral inhibition is used. Instead, the new method is based upon mutual information maximization between input patterns and competitive units. Thus, competition processes are flexibly controlled to produce explicit self-organizing maps. We applied our method to a road classification problem. Experimental results confirmed that the new method could produce more explicit self-organizing maps than conventional self-organizing methods.


Input Pattern Input Unit Cooperative Process Neighboring Neuron Competition Process 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ryotaro Kamimura
    • 1
  • Haruhiko Takeuchi
    • 2
  1. 1.Information Science Laboratory, and Future Science and Technology Joint Research CenterTokai UniversityHiratsuka KanagawaJapan
  2. 2.Human-Computer Interaction Group, Institute for Human Science and Biomedical EngineeringNational Institute of Advanced Industrial Science and TechnologyTsukubaJapan

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