CoTraining with Insufficient Views

5th Asian Conference on Machine Learning

数学科学;计算机科学

Wei Wang [e-mail] ; National Key Laboratory for Novel Software Technology

PID  :  123091

来源: CEUR

【 摘 要 】

Cotraining is a famous semisupervised learning paradigm exploiting unlabeled data with two views. Most previous theoretical analyses on cotraining are based on the assumption that each of the views is sufficient to correctly predict the label. However, this assumption can hardly be met in real applications due to feature corruption or various feature noise. In this paper, we present the theoretical analysis on cotraining when neither view is sufficient. We define the diversity between the two views with respect to the confidence of prediction and prove that if the two views have large diversity, cotraining is able to improve the learning performance by exploiting unlabeled data even with insufficient views. We also discuss the relationship between view insufficiency and diversity, and give some implications for understanding of the difference between cotraining and coregularization.