Multi-Task Compressive Sensing with Dirichlet Process Priors

author: Lawrence Carin, Department of Electrical and Computer Engineering, Duke University
published: Aug. 29, 2008,   recorded: July 2008,   views: 6231


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Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. In many applications, one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multi-task compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a principled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters.

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Comment1 Tom, September 9, 2008 at 2:45 p.m.:

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