Sparse recovery using sparse matrices
Citable URI:
http://hdl.handle.net/1721.1/40089
| Title: | Sparse recovery using sparse matrices |
| Author: | Berinde, Radu; Indyk, Piotr |
| Other Contributors: | Theory of Computation |
| Advisor: | Piotr Indyk |
| Issue Date: | 2008-01-10 |
| 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. |
| URI: | http://hdl.handle.net/1721.1/40089 |
| Other Identifiers: | MIT-CSAIL-TR-2008-001 |
| Related To |
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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Files in this item
| Files | Size | Format |
|---|---|---|
| MIT-CSAIL-TR-2008-001.pdf | 492.9Kb | application/pdf |
| MIT-CSAIL-TR-2008-001.ps | 1.991Mb | application/postscript |
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