AMP-B-SBL: An algorithm for clustered sparse signals using approximate message passing

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.