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Compressed sensing typically deals with the estimation of a system input from its noise-corrupted linear measurements, where the number of measurements is smaller than the number of input components. The performance of the estimation process is usually quantified by some standard error metric such as squared error or support set error. In this correspondence, we consider a noisy compressed sensing...
We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilistic measurement channel. The relaxed BP method is a Gaussian approximation of standard BP that offers significant computational savings for dense measurement matrices. The main contribution of this paper is to extend Guo and Wang's relaxed...
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