Multi-view semi-supervised classification via auto-weighted submarkov random walk

Semi-supervised classification aims to leverage a small amount of labeled data for learning tasks. Multi-view semi-supervised classification has attracted widespread attention because it can exploit multi-view data to optimize the classification performance. However, its methods are often ineffective when facing extremely limited labeled samples. In this paper, we propose a novel multi-view semi-supervised classification model via auto-weighted submarkov random walk. The proposed method can utilize similar nodes, spread information among nodes on graphs and exploit multi-view data with less labeled information. Accordingly, it enables an effective exploitation of both a small number of labeled data and a large amount of unlabeled data by connecting them to designed auxiliary nodes. Furthermore, an ideal weight on the Hellinger distance is allocated to each view data for obtaining a global label indicator matrix, which is expected to be robust to imbalanced classes. Compared with existing state-of-the-art methods, extensive experiments on six widely used datasets are conducted to verify the superiority of the proposed method.