DocumentCode
1487163
Title
Sparse Recovery Using Sparse Matrices
Author
Gilbert, Anna ; Indyk, Piotr
Author_Institution
Dept. of Math., Univ. of Michigan, Ann Arbor, MI, USA
Volume
98
Issue
6
fYear
2010
fDate
6/1/2010 12:00:00 AM
Firstpage
937
Lastpage
947
Abstract
In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas, including compressive sensing, data stream computing, and group testing.
Keywords
computational complexity; signal processing; sparse matrices; compressive sensing; computational complexity; data stream computing; group testing; sparse random matrices; sparse recovery problems; Computational complexity; Encoding; Hardware; Image coding; Mathematics; Signal processing; Signal processing algorithms; Sparse matrices; Testing; Vectors; Compressive sensing; expanders; sparse matrices; sparse recovery; streaming algorithms;
fLanguage
English
Journal_Title
Proceedings of the IEEE
Publisher
ieee
ISSN
0018-9219
Type
jour
DOI
10.1109/JPROC.2010.2045092
Filename
5462827
Link To Document