A sequential dictionary learning algorithm with enforced sparsity

Abstract

Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representations problems. These algorithms alternate between two stages: the sparse coding stage and dictionary update stage. In all existing dictionary learning algorithms the use of sparsity has been limited to the sparse coding stage while presenting differences in the dictionary update stage which can be achieved sequentially or in parallel. The singular value decomposition (SVD) has been successfully used for sequential dictionary update. In this paper we propose a dictionary learning algorithm that include a sparsity constraint also in the dictionary update stage. The cost function used to include sparsity in the dictionary update stage is derived using the link between SVD and rank one matrix approximation. The effectiveness of the proposed dictionary learning method is tested on synthetic data and an image processing application. The results reveal that including a sparsity constraint in the dictionary update stage is not a bad idea.