Diversity-promoting multi-view graph learning for semi-supervised classification

In this paper, we focus on how to boost the semi-supervised classification performance by exploring the multi-view graph learning. The key of multi-view graph learning is to learn a discriminative and informative graph from the multiple input graphs. However, we observe that existing multi-view graph learning methods do not sufficiently consider the diversity among views. This results in giving great weighted coefficients for mutually redundant views and affects the diversity of information of views utilized for multi-view graph learning, which finally deteriorates the semi-supervised classification performance. To address this issue, we propose a robust multi-view graph learning method with a novel and effective diversity-promoting regularized term to reduce the redundancy of views and enhance the diversity of the views. To improve the accuracy of label propagation, we further propose a unified framework which integrates multi-view graph learning, label propagation and diversity-promoting of views together. We develop an effective alternating optimization strategy to solve the optimization problem. Extensive experiments on synthetic and several benchmark data sets demonstrate the effectiveness of the proposed method.