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- W1986402544 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." @default.
- W1986402544 created "2016-06-24" @default.
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- W1986402544 date "2012-08-01" @default.
- W1986402544 modified "2023-09-26" @default.
- W1986402544 title "A generalized framework for learning and recovery of structured sparse signals" @default.
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- W1986402544 doi "https://doi.org/10.1109/ssp.2012.6319694" @default.
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