Efficient and robust discriminant dictionary pair learning for pattern classification

Abstract

Dictionary learning has been widely applied in the field of pattern classification. Although many analysis-synthesis dictionary pair learning methods have been proposed in recent years, most of them ignore the influence of noises and training their model is usually time-consuming. To overcome these issues, we propose an efficient and robust discriminant analysis-synthesis dictionary pair learning (ERDDPL) method for pattern classification, which can efficiently learn a structured analysis-synthesis dictionary pair with powerful discrimination and representation capabilities. Specifically, we design a coding coefficient discriminant term to ensure that the coding coefficient matrix has an approximate block diagonal structure, which can enhance the discrimination capability of the structured analysis dictionary. To weaken the influence of noises on the structured synthesis dictionary, we impose a low-rank constraint on each synthesis sub-dictionary. Besides, we develop a fast and efficient iterative algorithm to solve the optimization problem of ERDDPL. Extensive experiments on five image datasets sufficiently verify that our method has higher classification accuracy and efficiency than the state-of-the-art dictionary learning methods.