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Robust Blind Source Separation Utilizing Second and Fourth Order Statistics

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Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

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Abstract

We introduce identifiability conditions for the blind source separation (BSS) problem, combining the second and fourth order statistics. We prove that under these conditions, well known methods (like eigen-value decomposition and joint diagonalization) can be applied with probability one, i.e. the set of parameters for which such a method doesn’t solve the BSS problem, has a measure zero.

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Author information

Authors and Affiliations

  1. RIKEN, Brain Science Institute, Wako-shi, Saitama, 351-0198, Japan

    Pando Georgiev

  2. Sofia University “St. Kl. Ohridski”, Bulgaria

    Pando Georgiev

  3. RIKEN, Brain Science Institute, Wako-shi, Saitama, 351-0198, Japan

    Andrzej Cichocki

  4. Warsaw University of Technology, Poland

    Andrzej Cichocki

Authors
  1. Pando Georgiev
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  2. Andrzej Cichocki
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Editor information

Editors and Affiliations

  1. ETS Informática, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    José R. Dorronsoro

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© 2002 Springer-Verlag Berlin Heidelberg

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Georgiev, P., Cichocki, A. (2002). Robust Blind Source Separation Utilizing Second and Fourth Order Statistics. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_188

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  • DOI: https://doi.org/10.1007/3-540-46084-5_188

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

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