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Semi-blind approaches for source separation and independent component analysis

Babaie Zadeh, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. Publisher: d-side publication , 2006
  3. Abstract:
  4. This paper is a survey of semi-blind source separation approaches. Since Gaussian iid signals are not separable, simplest priors suggest to assume non Gaussian iid signals, or Gaussian non iid signals. Other priors can also been used, for instance discrete or bounded sources, positivity, etc. Although providing a generic framework for semi-blind source separation, Sparse Component Analysis and Bayesian ICA will just sketched in this paper, since two other survey papers develop in depth these approaches. © 2006 i6doc.com publication. All rights reserved
  5. Keywords:
  6. Gaussian distribution ; Independent component analysis ; Neural networks ; Surveys ; Bayesian ; Gaussians ; Generic frameworks ; Non-Gaussian ; Semi-blind approach ; Sparse component analysis ; Blind source separation
  7. Source: 14th European Symposium on Artificial Neural Networks, ESANN 2006, 26 April 2006 through 28 April 2006 ; 2006 , Pages 301-312 ; 2930307064 (ISBN); 9782930307060 (ISBN)
  8. URL: http://ee.sharif.edu/~bss/Esann2006.pdf