Titlebook: Compressed Sensing & Sparse Filtering; Avishy Y. Carmi,Lyudmila Mihaylova,Simon J. Godsil Book 2014 Springer-Verlag Berlin Heidelberg 2014

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ARCH

The Geometry of Compressed Sensing,instead of vector data, in many problems, data is more naturally expressed in matrix form (for example a video is often best represented in a pixel by time matrix). A powerful constraint on matrices are constraints on the matrix rank. For example, in low-rank matrix recovery, the goal is to reconstr

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Nonnegative Tensor Decomposition,mension less using the so-called t-product. A demonstration on an application in facial recognition shows the potential promise of the overall approach. We discuss a number of algorithmic options for solving the resulting optimization problems, and modification of such algorithms for increasing the

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companion

Sparse Nonlinear MIMO Filtering and Identification,acing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulations.

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hypotension

Sparsity and Compressed Sensing in Mono-Static and Multi-Static Radar Imaging,terns reflect spectral and spatial diversity trade-offs. Characterization of the expected quality of the reconstructed images in these scenarios prior to actual data collection is a problem of central interest in task planning for multi-mode radars. Compressed sensing theory argues that the mutual c

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ticlopidine

Konzepte zur Erfolgs- und Risikoanalyse,d as .-regularized . regression. We show that, under standard restricted isometry property (RIP) assumptions on the design matrix, .-minimization can provide stable recovery of a sparse signal in presence of exponential-family noise, and state some sufficient conditions on the noise distribution tha