Recently, we proposed an algorithm for the single measurement vector problem where the underlying sparse signal has an unknown clustered pattern. The algorithm is essentially a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. Treating the cluster pattern is controlled via a knob that accounts for the amount of clumpiness in the solution. The parameter corresponding to the knob is learned using expectation-maximization algorithm. In this paper, we provide further study by comparing the performance of our algorithm with other algorithms in terms of support recovery, mean-squared error, and an example in image reconstruction in a compressed sensing fashion.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5698263 | PMC |
| http://dx.doi.org/10.1109/UEMCON.2016.7777899 | DOI Listing |
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