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A Generalized Framework for Learning and Recovery of Structured Sparse Signals

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Title: A Generalized Framework for Learning and Recovery of Structured Sparse Signals
Creators: Ziniel, Justin
Advisor: Schniter, Philip
Issue Date: 2012-02
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 an example structured sparse signal model are provided.
Embargo: A one-year embargo was granted for this item.
Series/Report no.: 2012 Edward F. Hayes Graduate Research Forum. 26th
Keywords: compressed sensing
structured sparse signal recovery
sparse linear regression
message passing
approximate message passing
multiple measurement vector
loopy belief propagation
graphical models
probabilistic signal processing
Description: Engineering: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
URI: http://hdl.handle.net/1811/51771
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