Robust Bayesian sparse representation based on beta-Bernoulli process prior

Abstract

There has been a significant growing interest in the study of sparse representation recent years. Although many algorithms have been developed, outliers in the training data make the estimation unreliable. In the paper, we present a model under non-parametric Bayesian framework to solve the problem. The noise term in the sparse representation is decomposed into a Gaussian noise term and an outlier noise term, which we assume to be sparse. The beta-Bernoulli process is employed as a prior for finding sparse solutions.