Seeded random walk for multi-view semi-supervised classification.

Recently, multi-view learning has captured widespread attention in the machine learning area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data. Specifically, very limited work has been devoted to multi-view semi-supervised learning, where only a small number of labeled data points are available for model training. Therefore, a simple yet efficient seeded random walk scheme is proposed in this paper to address the multi-view semi-supervised classification problem, where known labeled data points serve as random seeds to be walked with certain probability. In this scheme, the semi-supervised classification indicator is obtained based primarily on an arrival probability and a reward matrix, which are computed by leveraging an initial distribution from some random seeds. Besides, theoretical analyses are then provided to indicate a connection of the proposed model with the existing manifold ranking method. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed model against compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, experimental results also suggest that the proposed method comes with positive robustness and promising generalization capability in terms of data classification.