DocumentCode :
3540543
Title :
A generalized framework for learning and recovery of structured sparse signals
Author :
Ziniel, Justin ; Rangan, Sundeep ; Schniter, Philip
Author_Institution :
ECE Dept., Ohio State Univ., Columbus, OH, USA
fYear :
2012
fDate :
5-8 Aug. 2012
Firstpage :
325
Lastpage :
328
Abstract :
We report on a framework for recovering single- or multi-timestep sparse signals that can learn and exploit a variety of probabilistic forms of structure. Message passing-based inference and empirical Bayesian parameter learning form the backbone of the recovery procedure. We further describe an object-oriented software paradigm for implementing our framework, which consists of assembling modular software components that collectively define a desired statistical signal model. Lastly, numerical results for synthetic and real-world structured sparse signal recovery are provided.
Keywords :
belief networks; learning (artificial intelligence); object-oriented programming; signal reconstruction; statistical analysis; empirical Bayesian parameter learning; generalized framework; learning; message passing-based inference; modular software components; multitimestep sparse signals; object-oriented software paradigm; real-world structured sparse signal recovery; single-sparse signals; statistical signal model; structured sparse signals; Compressed sensing; Correlation; Inference algorithms; Message passing; Object oriented modeling; Software; Vectors; compressed sensing; dynamic compressed sensing; multiple measurement vectors; structured sparse signal recovery; structured sparsity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location :
Ann Arbor, MI
ISSN :
pending
Print_ISBN :
978-1-4673-0182-4
Electronic_ISBN :
pending
Type :
conf
DOI :
10.1109/SSP.2012.6319694
Filename :
6319694
Link To Document :
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