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  3. Bayesian pursuit algorithm for sparse representation

Bayesian pursuit algorithm for sparse representation

Zayyani, H ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2009.4959892
  3. Publisher: 2009
  4. Abstract:
  5. In this paper, we propose a Bayesian Pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases. ©2009 IEEE
  6. Keywords:
  7. Bayesian ; Bayesian frameworks ; Bayesian hypothesis ; Compressed sensing (CS) ; Pursuit algorithms ; Simulation result ; Sparse component analysis (SCA) ; Sparse representation ; Acoustics ; Atoms ; Bayesian networks ; Signal processing ; Signal reconstruction ; Statistical tests ; Algorithms
  8. Source: 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 19 April 2009 through 24 April 2009 ; 2009 , Pages 1549-1552 ; 15206149 (ISSN); 9781424423545 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/4959892