Abstract: Dictionary learning is a hot topic in signal sparse decomposition. The choice of the initial dictionary affects the result of dictionary learning. In order to reduce the effects, a forgetting factor is introduced into the recursive least squares (RLS) dictionary learning. The dictionary learning effects of three dictionary
learning methods, method of optimal directions (MOD), K singular value decomposition (KSVD), and RLS are compared. Influences of different fixed forgetting factors on the final learned dictionary are analyzed,and the results of dictionary learning with different forgetting factors studied. Simulation shows that the RLS dictionary learning reduces the influence of the initial dictionary, and gives better effects. Results of the dictionary learning are influenced by the choice of the forgetting factor.

Key words: dictionary learning, sparse decomposition, recursive least squares (RLS), forgetting factor