DocumentCode
1517761
Title
Recovery Guarantees for Rank Aware Pursuits
Author
Blanchard, Jeffrey D. ; Davies, Mike E.
Author_Institution
Dept. of Math. & Stat, Grinnell Coll., Grinnell, IA, USA
Volume
19
Issue
7
fYear
2012
fDate
7/1/2012 12:00:00 AM
Firstpage
427
Lastpage
430
Abstract
This letter considers sufficient conditions for sparse recovery in the sparse multiple measurement vector (MMV) problem for some recently proposed rank aware greedy algorithms. Specifically we consider the compressed sensing framework with Gaussian random measurement matrices and show that the rank of the measurement matrix in the noiseless sparse MMV problem allows such algorithms to reduce the effect of the logn term that is present in traditional OMP recovery.
Keywords
computational complexity; greedy algorithms; matrix algebra; signal representation; Gaussian random measurement matrices; compressed sensing framework; noiseless sparse MMV problem; rank aware greedy algorithms; rank aware pursuits; signal representations; sparse multiple measurement vector problem; sparse recovery; traditional OMP recovery; Algorithm design and analysis; Joints; Matching pursuit algorithms; Multiple signal classification; Sparse matrices; Vectors; Greedy algorithm; multiple measurement vectors; orthogonal matching pursuit; rank;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2012.2199752
Filename
6200834
Link To Document