Bayesian Compressed Sensing via Belief Propagation,” Rice ECE Department

Bayesian Compressed Sensing via Belief Propagation,” Rice ECE Department (2006)

by Dror Baron , Shriram Sarvotham , Richard G. Baraniuk

Citations:

26 - 5 self

Datum	Value	Source
TITLE	Bayesian Compressed Sensing via Belief Propagation,” Rice ECE Department	INFERENCE
AUTHOR NAME	Dror Baron	SVM HeaderParse 0.2
AUTHOR NAME	Shriram Sarvotham	SVM HeaderParse 0.2
AUTHOR NAME	Richard G. Baraniuk	SVM HeaderParse 0.2
ABSTRACT	Abstract—Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length- signal containing large coefficients, our CS-BP decoding algorithm uses ( log ()) measurements and ( log 2 ()) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models. Index Terms—Bayesian inference, belief propagation, compressive sensing, fast algorithms, sparse matrices. I.	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report TREE 0601	INFERENCE
CITATIONS	60 found	ParsCit 1.0