Title :
Sparse Recovery Using Sparse Matrices
Author :
Gilbert, Anna ; Indyk, Piotr
Author_Institution :
Dept. of Math., Univ. of Michigan, Ann Arbor, MI, USA
fDate :
6/1/2010 12:00:00 AM
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;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2010.2045092
