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• W3100302110 abstract "We consider the recovery of an underlying signal x in C^m based on projection measurements of the form y=Mx+w, where y in C^l and w is measurement noise; we are interested in the case l < m. It is assumed that the signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is to design a projection matrix M in C^(l x m) to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections I(x;y) or the Renyi entropy of the projections h_a(y) (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projections matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we unveil the key operations carried out by the optimal projections designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projections design method with which state-of-the-art adaptive CS signal recovery is achieved." @default.
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• W3100302110 date "2012-01-01" @default.
• W3100302110 modified "2023-10-01" @default.
• W3100302110 title "Communications-Inspired Projection Design with Application to Compressive Sensing" @default.
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• W3100302110 doi "https://doi.org/10.1137/120878380" @default.
• W3100302110 hasPublicationYear "2012" @default.
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