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dc.contributor.advisorSchniter, Philip
dc.creatorZiniel, Justin
dc.date.accessioned2012-04-24T18:01:36Z
dc.date.available2012-04-24T18:01:36Z
dc.date.issued2012-02
dc.identifier.urihttp://hdl.handle.net/1811/51771
dc.descriptionEngineering: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)en_US
dc.description.abstractWe 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 an example structured sparse signal model are provided.en_US
dc.language.isoen_USen_US
dc.publisherSubmitted to 2012 IEEE Statistical Signal Processing Workshopen_US
dc.relation.ispartofseries2012 Edward F. Hayes Graduate Research Forum. 26then_US
dc.subjectcompressed sensingen_US
dc.subjectstructured sparse signal recoveryen_US
dc.subjecttime-seriesen_US
dc.subjectsparse linear regressionen_US
dc.subjectmessage passingen_US
dc.subjectapproximate message passingen_US
dc.subjectmultiple measurement vectoren_US
dc.subjectloopy belief propagationen_US
dc.subjectgraphical modelsen_US
dc.subjectprobabilistic signal processingen_US
dc.titleA Generalized Framework for Learning and Recovery of Structured Sparse Signalsen_US
dc.typePreprinten_US
dc.description.embargoA one-year embargo was granted for this item.en_US


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