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On the recovery of nonnegative sparse vectors from sparse measurements inspired by expanders
This paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. We focus on measurement matrices that are sparse, i.e., have only a constant number of nonzero (and non-negative) entries in e...
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Main Authors: | , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | This paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. We focus on measurement matrices that are sparse, i.e., have only a constant number of nonzero (and non-negative) entries in each column. For such measurement matrices we give a simple necessary and sufficient condition for l 1 optimization to successfully recover the unknown vector. Using a simple ldquoperturbationrdquo to the adjacency matrix of an unbalanced expander, we obtain simple closed form expressions for the threshold relating the ambient dimension n, number of measurements m and sparsity level k, for which l 1 optimization is successful with overwhelming probability. Simulation results suggest that the theoretical thresholds are fairly tight and demonstrate that the ldquoperturbationsrdquo significantly improve the performance over a direct use of the adjacency matrix of an expander graph. |
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ISSN: | 1520-6149 2379-190X |
DOI: | 10.1109/ICASSP.2009.4960227 |