Robust Blind Source Separation Utilizing Second and Fourth Order Statistics (original) (raw)

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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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
  2. Andrzej Cichocki

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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