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Task-driven adaptive statistical compressive sensing of gaussian mixture models

dc.contributor.author Carin, Lawrence
dc.contributor.author Duarte-Carvajalino, JM
dc.contributor.author Sapiro, Guillermo
dc.contributor.author Yu, G
dc.date.accessioned 2014-07-22T16:16:56Z
dc.date.issued 2013-01-21
dc.identifier.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10161/8951
dc.description.abstract A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.
dc.relation.ispartof IEEE Transactions on Signal Processing
dc.relation.isversionof 10.1109/TSP.2012.2225054
dc.title Task-driven adaptive statistical compressive sensing of gaussian mixture models
dc.type Journal article
pubs.begin-page 585
pubs.end-page 600
pubs.issue 3
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Mathematics
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 61


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