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Sparse recovery using sparse matrices

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dc.contributor.advisor Piotr Indyk en_US
dc.contributor.author Berinde, Radu en_US
dc.contributor.author Indyk, Piotr en_US
dc.contributor.other Theory of Computation en_US
dc.date.accessioned 2008-01-15T14:15:14Z
dc.date.available 2008-01-15T14:15:14Z
dc.date.issued 2008-01-10 en_US
dc.identifier.other MIT-CSAIL-TR-2008-001 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/40089
dc.description.abstract We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x from its lower-dimensional sketch Ax. A popular way of performing this recovery is by finding x* such that Ax=Ax*, and ||x*||_1 is minimal. It is known that this approach ``works'' if A is a random *dense* matrix, chosen from a proper distribution.In this paper, we investigate this procedure for the case where A is binary and *very sparse*. We show that, both in theory and in practice, sparse matrices are essentially as ``good'' as the dense ones. At the same time, sparse binary matrices provide additional benefits, such as reduced encoding and decoding time. en_US
dc.format.extent 13 p. en_US
dc.relation Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory en_US
dc.relation en_US
dc.title Sparse recovery using sparse matrices en_US


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