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A general approach for mutual information minimization and its application to blind source separation

Babaie Zadeh, M ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1016/j.sigpro.2004.11.021
  3. Publisher: Elsevier , 2005
  4. Abstract:
  5. In this paper, a nonparametric "gradient" of the mutual information is first introduced. It is used for showing that mutual information has no local minima. Using the introduced "gradient", two general gradient based approaches for minimizing mutual information in a parametric model are then presented. These approaches are quite general, and principally they can be used in any mutual information minimization problem. In blind source separation, these approaches provide powerful tools for separating any complicated (yet separable) mixing model. In this paper, they are used to develop algorithms for separating four separable mixing models: linear instantaneous, linear convolutive, post nonlinear (PNL) and convolutive post nonlinear (CPNL) mixtures. © 2005 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Algorithms ; Blind source separation ; Conformal mapping ; Convergence of numerical methods ; Differentiation (calculus) ; Estimation ; Independent component analysis ; Integral equations ; Mathematical models ; Set theory ; Signal processing ; Vectors ; Convolutive mixtures ; Convolutive post nonlinear (CPNL) mixtures ; Gradient of mutual information ; Information theoretic learning ; Mutual information ; Post nonlinear (PNL) mixtures ; Score function difference (SFD) ; Information theory
  8. Source: Signal Processing ; Volume 85, Issue 5 SPEC. ISS , 2005 , Pages 975-995 ; 01651684 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0165168405000186