Dictionary Learning for sparse representation based on nuclear norm minimization

摘要

This article gives a new procedure for designing dictionaries in order to represent signals sparsely. From the given set of training signals, the procedure learns to find a sparse representation of the signals by nuclear norm minimization. This method is closely related to the problem of low-rank matrix completion.