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Application of the MEC network to principal component analysis and source separation

  • Part IV: Signal Processing: Blind Source Separation, Vector Quantization, and Self-Organization
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

In this paper we present new developments of a previous work dealing with the problem of strongly-constrained orthonormal analysis of random signals. In the former work a neural learning rule arising from the study of the dynamics of a massive system in an abstract space was introduced, and the set of equations describing the motion of such a system was directly interpreted as a learning rule for neural layers. This adaptation rule can be used to solve several problems where orthonormal matrices are involved. Here we show two applications of such an approach: one dealing with PCA and one dealing with ICA.

This research was supported by the Italian MURST. Please send comments and suggestions to the first author.

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Authors and Affiliations

  1. Dipartimento di Elettronica e Automatica, Università di Ancona, Via Brecce Bianche, 60131, An, Italy

    Simone Fiori, Aurelio Uncini & Francesco Piazza

Authors
  1. Simone Fiori
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  2. Aurelio Uncini
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  3. Francesco Piazza
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Fiori, S., Uncini, A., Piazza, F. (1997). Application of the MEC network to principal component analysis and source separation. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020215

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  • DOI: https://doi.org/10.1007/BFb0020215

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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