A generalized framework for learning and recovery of structured sparse signals

Justin Ziniel, Sundeep Rangan, Philip Schniter

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages325-328
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
CountryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

Keywords

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

ASJC Scopus subject areas

  • Signal Processing

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