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Sparse component analysis in presence of noise using an iterative EM-MAP algorithm

Zayyani, H ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-74494-8_55
  3. Publisher: Springer Verlag , 2007
  4. Abstract:
  5. In this paper, a new algorithm for source recovery in under-determined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of under-determined systems of linear equations with additive Gaussian noise. The method is based on iterative Expectation-Maximization of a Maximum A Posteriori estimation of sources (EM-MAP) and a new steepest-descent method is introduced for the optimization in the Mstep. The solution obtained by the proposed algorithm is compared to the minimum ℓ1-norm solution achieved by Linear Programming (LP). It is experimentally shown that the proposed algorithm is about one order of magnitude faster than the interior-point LP method, while providing better accuracy. © Springer-Verlag Berlin Heidelberg 2007
  6. Keywords:
  7. Additive noise ; Blind source separation ; Gaussian noise (electronic) ; Iterative methods ; Problem solving ; Signal processing ; Expectation-Maximization of a Maximum A Posteriori estimation of sources (EM-MAP) ; Sparse component analysis ; Sparse decomposition ; Steepest descent method ; Independent component analysis
  8. Source: 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, London, 9 September 2007 through 12 September 2007 ; Volume 4666 LNCS , 2007 , Pages 438-445 ; 03029743 (ISSN); 9783540744931 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-540-74494-8_55