COMBINING GEOMETRY AND COMBINATORICS: A UNIFIED APPROACH TO SPARSE SIGNAL RECOVERY

by R. Berinde , A. C. Gilbert , P. Indyk , H. Karloff , M. J. Strauss

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42 - 11 self

Datum	Value	Source
TITLE	COMBINING GEOMETRY AND COMBINATORICS: A UNIFIED APPROACH TO SPARSE SIGNAL RECOVERY	SVM HeaderParse 0.2
AUTHOR NAME	R. Berinde	SVM HeaderParse 0.2
AUTHOR NAME	A. C. Gilbert	SVM HeaderParse 0.2
AUTHOR NAME	P. Indyk	SVM HeaderParse 0.2
AUTHOR NAME	H. Karloff	SVM HeaderParse 0.2
AUTHOR NAME	M. J. Strauss	SVM HeaderParse 0.2
ABSTRACT	Abstract. There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach starts with a geometric constraint on the measurement matrix Φ and then uses linear programming to decode information about x from Φx. The combinatorial approach constructs Φ and a combinatorial decoding algorithm to match. We present a unified approach to these two classes of sparse signal recovery algorithms. The unifying elements are the adjacency matrices of high-quality unbalanced expanders. We generalize the notion of Restricted Isometry Property (RIP), crucial to compressed sensing results for signal recovery, from the Euclidean norm to the ℓp norm for p ≈ 1, and then show that unbalanced expanders are essentially equivalent to RIP-p matrices. From known deterministic constructions for such matrices, we obtain new deterministic measurement matrix constructions and algorithms for signal recovery which, compared to previous deterministic algorithms, are superior in either the number of measurements or in noise tolerance. 1.	SVM HeaderParse 0.2
CITATIONS	38 found	ParsCit 1.0