Abstract:We study the problem of sparse recovery in an overcomplete dictionary. This problem has attracted considerable attention in signal processing, statistics, and computer science, and a variety of algori...We study the problem of sparse recovery in an overcomplete dictionary. This problem has attracted considerable attention in signal processing, statistics, and computer science, and a variety of algorithms have been developed to recover the sparse vector. We propose a new method based on the computationally efficient Viterbi algorithm which is shown to achieve better performance than competing algorithms such as Orthogonal Matching Pursuit (OMP), Orthogonal Least-Squares (OLS), Multi-Branch Matching Pursuit (MBMP), Iterative Hard Thresholding (IHT), and l1 minimization. We also explore the relationship of the Viterbi-based approach with OLS.Read More
Publication Year: 2014
Publication Date: 2014-10-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 11
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