%A Cristian Rusu
%T Fast design of efficient dictionaries for sparse representations
%R 10.1109/MLSP.2012.6349795
%I IEEE
%L eprints1528
%P 1-5
%B IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012
%X One of the central issues in the field of sparse representations is the design of overcomplete dictionaries with a fixed sparsity level from a given dataset. This article describes a fast and efficient procedure for the design of such dictionaries. The method implements the following ideas: a reduction technique is applied to the initial dataset to speed up the upcoming procedure; the actual training procedure runs a more sophisticated iterative expanding procedure based on K-SVD steps. Numerical experiments on image data show the effectiveness of the proposed design strategy.
%D 2012
%K K-SVD algorithm, T-mindot, clustering, large datasets training, representation errors, signal processing, sparse representations