Latent multi-view semi-supervised classification by using graph learning

Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local structure of this latent intact representation. To fully use this latent intact representation to discover the label information of the unlabeled data, we consider to unify the procedures of computing the latent intact representation and the labels of unlabeled data as a whole. An alternating optimization algorithm is designed to effectively solve the optimization of the proposed method. Extensive experimental results demonstrate the effectiveness of our proposed method.