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Building nonlinear data models with self-organizing maps

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

We study the extraction of nonlinear data models in high dimensional spaces with modified self-organizing maps. Our algorithm maps lower dimensional lattice into a high dimensional space without topology violations by tuning the neighborhood widths locally. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. The performance of the algorithm is demonstrated for one- and two-dimensional principal manifolds and for sparse data sets.

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Author information

Authors and Affiliations

  1. Institute of Informatics, University of Leipzig, P. O. Box 920, 04109, Leipzig, Germany

    Ralf Der & Gerd Balzuweit

  2. Lab. of Information Representation, RIKEN, 2-1 Hirosawa, Wako-shi, 351-01, Saitama, Japan

    Michael Herrmann

Authors
  1. Ralf Der
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  2. Gerd Balzuweit
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  3. Michael Herrmann
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Der, R., Balzuweit, G., Herrmann, M. (1996). Building nonlinear data models with self-organizing maps. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_138

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  • DOI: https://doi.org/10.1007/3-540-61510-5_138

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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