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.
Keywordsmulti-view learning semi-supervised learning regularization machine learning
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