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International Conference on Artificial Neural Networks

ICANN 1996: Artificial Neural Networks — ICANN 96 pp 139–144Cite as

Purely local neural Principal Component and Independent Component Learning

  • Oral Presentations: Theory Theory VII: Unsupervised Learning
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Abstract

New algorithms for neural Principal Component and Independent Component Analysis (PCA and ICA) are introduced. Special emphasis is laid on the locality of learning. This enables a simpler hardware implementation, and may provide a more plausible model of biological neurons. To achieve this, the algorithms feature a new kind of feedback which is multiplicative and anti-Hebbian. The convergence of the ICA algorithm is proven analytically in the general case; the convergence of the PCA algorithm is proven for Gaussian data.

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Author information

Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinki University of Technology, Rakentajanaukio 2 C, FIN-02150, Espoo, Finland

    Aapo Hyvärinen

Authors
  1. Aapo Hyvärinen
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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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Hyvärinen, A. (1996). Purely local neural Principal Component and Independent Component Learning. 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_27

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  • DOI: https://doi.org/10.1007/3-540-61510-5_27

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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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