A Sequential Block-Structured Dictionary Learning Algorithm for Block Sparse Representations

Abstract

Dictionary learning algorithms have been successfully applied to a number of signal and image processing problems. In some applications, however, the observed signals may further have a multisubspace structure that enables block-sparse signal representations. For this case, a new algorithm for learning blockstructured dictionaries for block-sparse signal representations is proposed in this paper. It is obtained by solving a sequence of penalized low rank matrix approximation problems where the ℓ 1,2 - norm is introduced as a penalty promoting block sparsity and then using a block coordinate descent approach to estimate the unknowns. The proposed algorithm has the advantage of involving simpl..