A multi-view regularization method for semi-supervised learning

Authors:
Jiao Wang;Siwei Luo;Yan Li
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

Multi-view semi-supervised learning is a hot research topic recently In this paper, we consider the regularization problem in multi-view semi-supervised learning A regularization method adaptive to the given data is proposed, which can use unlabeled data to adjust the degree of regularization automatically This new regularization method comprises two levels of regularization simultaneously Experimental evidence on real word dataset shows its effectivity.