A generalized framework for learning and recovery of structured sparse signals

2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Volumn , Issue , 2012, Pages 325-328

  Ziniel, Justin ^a   Rangan, Sundeep ^b   Schniter, Philip ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b POLYTECHNIC UNIVERSITY   (United States)

Author keywords

compressed sensing; dynamic compressed sensing; multiple measurement vectors; structured sparse signal recovery; structured sparsity

Indexed keywords

COMPRESSIVE SENSING; MODULAR SOFTWARES; MULTIPLE MEASUREMENT VECTORS; NUMERICAL RESULTS; OBJECT ORIENTED SOFTWARE; PARAMETER LEARNING; RECOVERY PROCEDURE; SIGNAL MODELS; SPARSE SIGNALS; STRUCTURED SPARSITY;

RECOVERY;

SIGNAL RECONSTRUCTION;

EID: 84868222706     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2012.6319694     Document Type: Conference Paper

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