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Sparse recovery using sparse matrices

dc.date.accessioned2008-01-15T14:15:14Z
dc.date.accessioned2018-11-24T10:27:46Z
dc.date.available2008-01-15T14:15:14Z
dc.date.available2018-11-24T10:27:46Z
dc.date.issued2008-01-10en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/40089
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/40089
dc.description.abstractWe 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.extent13 p.en_US
dc.relationMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratoryen_US
dc.relationen_US
dc.titleSparse recovery using sparse matricesen_US


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