Discriminative Multiview Nonnegative Matrix Factorization for Classification

Abstract

Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination. In this paper, we introduce a novel discriminative multiview nonnegative matrix (DMultiNMF) algorithm to learn discriminative and consistent representations for facilitating classification. In this algorithm, we apply discriminative patch alignment to enhance the local discrimination in each view and utilize the large margin principle to improve global discrimination. At the same time, we use a shared representation to propagate information among the multiple views to ensure consistency. Apart from that, we measure the reconstruction errors utilizing the correntropy-induced metric to improve the robustness. The experiments on face recognition, handwritten digit recognition, Xmedia, and Wikipedia multiview data sets demonstrate the advantages of the proposed method compared with other algorithms like single view using concatenated views and substantially better than other multiview nonnegative matrix factorization algorithms.