Parallel block sparse Bayesian learning for high dimensional sparse signals

Abstract

We address the recovery of block sparse signals by proposing a distributed solution that uses a block-diagonal approximation to the dictionary matrix of the problem. The approximation is found in two stages. First, the Gram matrix of the dictionary matrix is used as a basis for spectral clustering. Afterwards, measurement positions are assigned to the clusters formed from this spectral clustering. The method is then applied to use previous algorithms in the literature of Block Sparse Bayesian Learning in parallel. Moreover, this method also speeds up the algorithm in serial systems. The efficacy of the proposed method is demonstrated in simulations with comparison to the previous Block Sparse Bayesian Learning algorithms.