A Generalized Framework for Learning and Recovery of Structured Sparse Signals
structured sparse signal recovery
sparse linear regression
approximate message passing
multiple measurement vector
loopy belief propagation
probabilistic signal processing
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Publisher:Submitted to 2012 IEEE Statistical Signal Processing Workshop
Series/Report no.:2012 Edward F. Hayes Graduate Research Forum. 26th
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.
Engineering: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
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