Thresholding-based online algorithms of complexity comparable to sparse LMS methods

Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Stephen McLaughlin

Abstract: This paper deals with a novel class of set-theoretic adaptive sparsity promoting algorithms of linear computational complexity. Sparsity is induced via generalized thresholding operators, which correspond to nonconvex penalties such as those used in a number of sparse LMS based schemes. The results demonstrate the significant performance gain of our approach, at comparable computational cost.