A generalized framework for learning and recovery of structured sparse signals

Justin Ziniel; Sundeep Rangan; Philip Schniter

doi:10.1109/SSP.2012.6319694

A generalized framework for learning and recovery of structured sparse signals

Justin Ziniel, Sundeep Rangan, Philip Schniter

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages	325-328
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2012.6319694
State	Published - 2012
Event	2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States Duration: Aug 5 2012 → Aug 8 2012

Publication series

Name	2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Other

Other	2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/Territory	United States
City	Ann Arbor, MI
Period	8/5/12 → 8/8/12

Keywords

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

ASJC Scopus subject areas

Signal Processing

Access to Document

10.1109/SSP.2012.6319694

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Ziniel, J, Rangan, S & Schniter, P 2012, A generalized framework for learning and recovery of structured sparse signals. in 2012 IEEE Statistical Signal Processing Workshop, SSP 2012., 6319694, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, pp. 325-328, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 8/5/12. https://doi.org/10.1109/SSP.2012.6319694

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language = "English (US)",

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AB - 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.

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KW - multiple measurement vectors

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KW - structured sparsity

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M3 - Conference contribution

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T3 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

SP - 325

EP - 328

BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

T2 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Y2 - 5 August 2012 through 8 August 2012

ER -

A generalized framework for learning and recovery of structured sparse signals

Abstract

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Other

Keywords

ASJC Scopus subject areas

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