| 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.description.provenance |
Submitted by CSAIL Importer (publications-dspace@csail.mit.edu) on 2008-01-15T14:15:12Z
No. of bitstreams: 2
MIT-CSAIL-TR-2008-001.pdf: 492912 bytes, checksum: f005f385e88153d325900c636fcfcc42 (MD5)
MIT-CSAIL-TR-2008-001.ps: 1991890 bytes, checksum: 3b0f5642bc06fafcaedef7130fd59b35 (MD5) |
en |
| dc.description.provenance |
Made available in DSpace on 2008-01-15T14:15:14Z (GMT). No. of bitstreams: 2
MIT-CSAIL-TR-2008-001.pdf: 492912 bytes, checksum: f005f385e88153d325900c636fcfcc42 (MD5)
MIT-CSAIL-TR-2008-001.ps: 1991890 bytes, checksum: 3b0f5642bc06fafcaedef7130fd59b35 (MD5)
Previous issue date: 2008-01-10 |
en |
| 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 |
