A New Analysis of Co-Training

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 possibility to bring the graph-based and disagreement-based 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 tex tit 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.