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July 25, 2014

Mtg: Bayesian Methods for Sparse Signal Recovery and Compressed Sensing

by @ 4:38 pm. Filed under ALL, Communications
 

THURSDAY August 14, 2014
SCV Signal Processing Chapter
– priors, estimations, empirical, correlation …
Speaker: Prof. Bhaskar D. Rao, ECE Dept, UC-San Diego
Time: Networking/Light Dinner at 6:30 PM; Presentation at 7:00 PM
Cost: donation accepted for food
Place: AMD (Commons Bldg), 1 AMD Place, Sunnyvale
RSVP: from website
Web: www.ewh.ieee.org/r6/scv/sps

Compressive sensing (CS) as an approach for data acquisition has recently received much attention.  In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations.  The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk.  The main emphasis will be on Bayesian approaches for sparse signal recovery.  We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods.  In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed.  The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method.  If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem.

20140814scv

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