Bayesian Pursuit algorithm for sparse representation

  • Authors:

  • H. Zayyani;M. Babaie-Zadeh;C. Jutten

  • Affiliations:

  • Sharif University of Technology, Department of Electrical Engineering and Advanced Communication Research Institute, Tehran, Iran;Sharif University of Technology, Department of Electrical Engineering and Advanced Communication Research Institute, Tehran, Iran;GIPSA-LAB, Grenoble, France

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

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.