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Quasi-optimal EASI algorithm based on the Score Function Difference (SFD)

Samadi, S ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2005.12.019
  3. Publisher: 2006
  4. Abstract:
  5. Equivariant adaptive separation via independence (EASI) is one of the most successful algorithms for blind source separation (BSS). However, the user has to choose non-linearities, and usually simple (but non-optimal) cubic polynomials are applied. In this paper, the optimal choice of these non-linearities is addressed. We show that this optimal non-linearity is the output score function difference (SFD). Contrary to simple non-linearities usually used in EASI (such as cubic polynomials), the optimal choice is neither component-wise nor fixed: it is a multivariate function which depends on the output distributions. Finally, we derive three adaptive algorithms for estimating the SFD and achieving "quasi-optimal" EASI algorithms, whose separation performance is much better than "standard" EASI and which especially converges for any sources. © 2006 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Adaptive algorithms ; Independent component analysis ; Nonlinear systems ; Optimization ; Polynomials ; Equivariant adaptive separation via independence (EASI) ; Multivariate function ; Score function difference (SFD) ; Blind source separation ; Algorithm ; Analytical error ; Intermethod comparison ; Mathematical analysis ; Mathematical computing ; Maximum likelihood method ; Multivariate analysis ; Nonlinear system ; Priority journal ; Scoring system ; Signal processing
  8. Source: Neurocomputing ; Volume 69, Issue 13-15 , 2006 , Pages 1415-1424 ; 09252312 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0925231205003346