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Enhancing the performance of the Bayesian Pursuit Algorithm

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2 Author(s)
B. Deka ; Department of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, 781039, India ; P. K. Bora

Finding sparse solutions to under-determined systems of linear equations has recently got a plethora of applications in the field of signal processing. It is assumed that an ideal noiseless signal has sufficiently sparse representation. But in practice a noisy version of such signal can only be observed. In this paper, we propose a new initialization scheme and a stopping condition for the recently introduced Bayesian Pursuit Algorithm (BPA) for sparse representation in the noisy settings. Experimental results show that the proposed modifications lead to a better quality of sparse solution and faster rate of convergence over the existing BPA especially at low noise levels.

Published in:

Communications (NCC), 2011 National Conference on

Date of Conference:

28-30 Jan. 2011