Synthesis linear classifier based analysis dictionary learning for pattern classification

Abstract Dictionary learning approaches have been widely applied to solve pattern classification problems and have achieved promising performance. However, most of works aim to learn a discriminative synthesis dictionary and sparse coding coefficients for classification. Until recent years, analysis dictionary learning began to attract interest from researchers. In this paper, we present a novel discriminative analysis dictionary learning frame, named Synthesis Linear Classifier based Analysis Dictionary Learning (SLC-ADL). Firstly, we incorporate a synthesis-linear-classifier-based error term into the basic analysis dictionary learning model, whose classification performance is obviously improved by making full use of the label information. Then, we develop an alternating iterative algorithm to solve the new model and obtain closed-form solutions leading to pretty competitive running efficiency. What is more, we design three classification schemes by fully exploiting the synthesis linear classifier. Finally, extensive comparison experiments on scene categorization, object classification, action recognition and face recognition clearly verify the classification performance of the proposed algorithm.