An initialization strategy for the dictionary learning problem

In this paper we present an efficient initialization strategy that improves the performance of overcomplete dictionary learning algorithms. The procedure exploits incoherent structures that can be manipulated and adapted to a given dataset relatively fast. The algorithm involves an iterative adaptation of the dictionary to the dataset with pruning of the less used atoms and constructions of new atoms that fit the data better. Experimental simulations show that the proposed method improves the performance of classical and new developments in dictionary learning algorithms.