Sparse recovery using sparse matrices
dc.date.accessioned | 2008-01-15T14:15:14Z | |
dc.date.accessioned | 2018-11-24T10:27:46Z | |
dc.date.available | 2008-01-15T14:15:14Z | |
dc.date.available | 2018-11-24T10:27:46Z | |
dc.date.issued | 2008-01-10 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/40089 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/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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