Discriminative neighborhood preserving dictionary learning for image classification

Abstract

In this paper, a discriminative neighborhood preserving dictionary learning method is proposed. The geometrical structure of the feature space is used to preserve the similarity information of the features, and the features’ class information is employed to enhance the discriminative power of the learned dictionary. The Laplacian matrix which expresses the similarity information and the class information of the features is constructed and used in the objective function. Experimental results on four public datasets demonstrate the effectiveness of the proposed method.