论文信息 - A Multi-view Regularization Method for Semi-supervised Learning

A Multi-view Regularization Method for Semi-supervised Learning

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.

[1] Dale Schuurmans,et al. Metric-Based Methods for Adaptive Model Selection and Regularization , 2002, Machine Learning.

[2] Bernhard Schölkopf,et al. Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.

[3] Vladimir Vapnik,et al. Statistical learning theory , 1998 .

[4] Sanjoy Dasgupta,et al. PAC Generalization Bounds for Co-training , 2001, NIPS.

[5] Mikhail Belkin,et al. A Co-Regularization Approach to Semi-supervised Learning with Multiple Views , 2005 .

[6] Yan Peng,et al. Semi-Supervised Canonical Correlation Analysis Algorithm: Semi-Supervised Canonical Correlation Analysis Algorithm , 2009 .

[7] Zhang Dao. Semi-Supervised Canonical Correlation Analysis Algorithm , 2008 .

[8] Sham M. Kakade,et al. Multi-view Regression Via Canonical Correlation Analysis , 2007, COLT.

[9] Qiang Yang,et al. Semi-Supervised Learning with Very Few Labeled Training Examples , 2007, AAAI.

[10] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.

[11] Zhi-Hua Zhou,et al. Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.