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Ramesh, Lekshmi and Murthy, Chandra R (2018) SPARSE SUPPORT RECOVERY VIA COVARIANCE ESTIMATION. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), APR 15-20, 2018, Calgary, CANADA, pp. 6633-6637.

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Official URL: http://dx.doi.org/10.1109/ICASSP.2018.8462509


We consider the problem of recovering the common support of a set of k-sparse signals {X-i}(i=1)(L) from noisy linear under-determined measurements of the form {Phi X-i + w(i)}(i=1)(L) where Phi is an element of R-mxN (m < N) is the sensing matrix and w(i )is the additive noise. We employ a Bayesian setup where we impose a Gaussian prior with zero mean and a common diagonal covariance matrix Gamma across all x(i), and formulate the support recovery problem as one of covariance estimation. We develop an algorithm to find the approximate maximum-likelihood estimate of Gamma using a modified reweighted minimization procedure. Empirically, we find that the proposed algorithm succeeds in exactly recovering the common support with high probability in the k < m regime with L of the order of m and in the k >= m regime with larger L. The key advantage of the proposed algorithm is that its complexity is independent of L, unlike existing sparse support recovery algorithms.

Item Type: Conference Proceedings
Publisher: IEEE
Additional Information: Copy right for this article belong to IEEE
Keywords: Sparse support recovery; covariance estimation; multiple measurement vectors
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 26 Oct 2018 14:44
Last Modified: 26 Oct 2018 14:44
URI: http://eprints.iisc.ac.in/id/eprint/60967

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