Discriminative dictionary pair learning from partially labeled data

Yi Rong, Shengwu Xiong, Yongsheng Gao

Abstract: While conventional synthesis dictionary learning approaches have demonstrated tremendous success in various pattern recognition problems, the dictionary pair learning, i.e., jointly learning an analysis dictionary and a synthesis dictionary is still an open problem. Furthermore, the performance of traditional supervised dictionary learning methods is often limited by the amount of labeled training data. In this paper, we propose a novel dictionary pair learning model by utilizing both labeled and unlabeled data for analysis-synthesis dictionary training. In the dictionary learning phase, we integrate the unlabeled samples, whose labels are predicted through an entropy-based method, into their associated classes to increase the amount of the `labeled' data. This strategy promotes the discrimination power of both analysis dictionary and synthesis dictionary. Experimental evaluations on publicly available datasets demonstrate the usefulness of semi-supervised strategy and the effectiveness of the proposed method, especially in the case of limited number of the labeled samples.