Accelerated manifold embedding for multi-view semi-supervised classification

Abstract

Multi-view semi-supervised learning has gained much attention since a great number of unlabeled multi-view data are easy to obtain while few labeled data are available. Accordingly, how to utilize the relationship between labeled and unlabeled data is of significance in multi-view semi-supervised learning. In this paper, we propose an auto-weighted manifold embedding model to address multi-view semi-supervised classification problems, where only a small percentage of labeled data points are used for model training. In the proposed model, data points close to each other in the feature space will be assigned to similar classes in the label space through manifold embedding. Accordingly, the class information of labeled data will be employed in the prediction process for unlabeled data. Moreover, an optimal weight for each view of multi-view data is learned automatically, which enables an encouraging fusion quality for multi-view manifold embedding. To further speed up the calculation process and reduce the computational complexity of the proposed model, an effective accelerated auto-weighted manifold embedding scheme is developed. Besides, theoretical analyses are then provided to indicate a tight approximation bound for the primary manifold embedding method, while the accelerated method is designed by an order decrease of magnitude of computational complexity. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed models over compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, the experimental results are suggestive of positive robustness and promising generalization capacity of the proposed methods.