Task-driven adaptive statistical compressive sensing of gaussian mixture models

Julio M. Duarte-Carvajalino, Guoshen Yu, Lawrence Carin, Guillermo Sapiro

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)585-600
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - Jan 21 2013
Externally publishedYes


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