A general framework for co-training and its applications

Co-training is one of the major semi-supervised learning paradigms in which two classifiers are alternately trained on two distinct views and they teach each other by adding the predictions of unlabeled data to the training set of the other view. Co-training can achieve promising performance, especially when there is only a small number of labeled data. Hence, co-training has received considerable attention, and many variant co-training algorithms have been developed. It is essential and informative to provide a systematic framework for a better understanding of the common properties and differences in these algorithms. In this paper, we propose a general framework for co-training according to the diverse learners constructed in co-training. Specifically, we provide three types of co-training implementations, including co-training on multiple views, co-training on multiple classifiers, and co-training on multiple manifolds. Finally, comprehensive experiments of different methods are conducted on the UCF-iPhone dataset for human action recognition and the USAA dataset for social activity recognition. The experimental results demonstrate the effectiveness of the proposed solutions.