Abstract

In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training process is viewed as a combinative label propagation over two views; this provides a possibility to bring the graph-based and disagreementbased semi-supervised methods into a unified framework. With the analysis we get some insight that has not been disclosed by previous theoretical studies. In particular, we provide the sufficient and necessary condition for co-training to succeed. We also discuss the relationship to previous theoretical results and give some other interesting implications of our results, such as combination of weight matrices and view split.