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During the last decade, there has been a growing interest toward the problem of sparse decomposition. A very important task in this field is dictionary learning, which is designing a suitable dictionary that can sparsely represent a group of training signals. In most dictionary learning algorithms, the cost function to determine the the optimum dictionary is the 0 norm of the matrix of decomposition coefficients of the training signals. However, we believe that this cost function fails to fullydoi:10.1109/icassp.2013.6638787 dblp:conf/icassp/SadeghipoorBJ13 fatcat:73mai3h72fekrmqggpmitqxbei