Group-sparse adaptive variational Bayes estimation

Abstract

This paper presents a new variational Bayes algorithm for the adaptive estimation of signals possessing group structured sparsity. The proposed algorithm can be considered as an extension of a recently proposed variational Bayes framework of adaptive algorithms that utilize heavy tailed priors (such as the Student-t distribution) to impose sparsity. Variational inference is efficiently implemented via appropriate time recursive equations for all model parameters. Experimental results are provided that demonstrate the improved estimation performance of the proposed adaptive group sparse variational Bayes method, when compared to state-of-the-art sparse adaptive algorithms.