Advertisement

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)

Abstract

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.

Keywords

Input Pattern Input Unit Cooperative Process Neighboring Neuron Competition Process 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Linsker, R.: Self-organization in a perceptual network. Computer 21, 105–117 (1988)CrossRefGoogle Scholar
  2. 2.
    Linsker, R.: How to generate ordered maps by maximizing the mutual information between input and output. Neural Computation 1, 402–411 (1989)CrossRefGoogle Scholar
  3. 3.
    Kaski, S., Nikkila, J., Kohonen, T.: Methods for interpreting a self-organized map in data analysis. In: Proceedings of European Symposium on Artificial Neural Networks, Bruges, Belgium (1998)Google Scholar
  4. 4.
    Kamimura, R., Kamimura, T., Takeuchi, H.: Greedy information acquisition algorithm: A new information theoretic approach to dynamic information acquisition in neural networks. Connection Science (2002)Google Scholar
  5. 5.
    Gatlin, L.L.: Information Theory and Living Systems. Columbia University Press, Columbia (1972)Google Scholar
  6. 6.
    Kohonen, T.: Self-Organization Maps. Springer, Heidelberg (1995)Google Scholar
  7. 7.
    Marc, M., Hulle, M.V.: Faithful representations and topographic maps. John Wiley and Sons, Inc, New York (2000)Google Scholar

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

Personalised recommendations