Maximum a posteriori estimation approach to sparse recovery

Title: Maximum a posteriori estimation approach to sparse recovery
Creator: Hyder, Md Mashud; Mahata, Kaushik
Relation: 2011 17th International Conference on Digital Signal Processing. Proceedings of 2011 17th International Conference on Digital Signal Processing (Corfu, GR 6-8 July, 2011)
Publisher Link: http://dx.doi.org/10.1109/ICDSP.2011.6004892
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2011
Description: We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a small number of measurements formed by computing the inner products of the signal with rows of a matrix. We assume that each component of the sparse signal is independent and identically distributed (i.i.d) random variable drawn from a Gaussian mixture model. We then develop a suitable MAP formulation which results in an iterative algorithm. Simulations are performed to study the performance of the algorithm. We observe that our approach has a number of advantages over other sparse recovery techniques, including robustness to noise, increased performance with limited measurements and lower computation time.
Subject: maximum a posteriori estimation; Gaussian mixture model; sparse signal; basis pursuit
Identifier: http://hdl.handle.net/1959.13/1325372
Identifier: uon:25255
Identifier: ISBN:9781457702730
Language: eng
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