基于流形正则化的半监督投影双子支持向量机<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201602001

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Abstract

Projection twin support vector machine (PTSVM) is a supervised learning method and its performance deteriorates when supervised information is insufficient. To resolve this issue, a semisupervised projection twin support vector machine (SPTSVM) is proposed inspired by the manifold regularization. Both supervised (labeled) and unsupervised (unlabeled) information are utilized to build a more reasonable semisupervised classifier. Compared with PTSVM, SPTSVM takes the intrinsic geometric information into full consideration via manifold regularization. Furthermore, by selecting appropriate parameters, SPTSVM degenerates into either supervised PTSVM or projection twin support vector machine with regularization term. The effectiveness of the proposed approach is demonstrated by comparison on both artificial and realworld datasets.

Key words： Semi-supervised Learning Support Vector Machine Projection Twin Support Vector Machine (PTSVM) Manifold Regularization Nonparallel Projection

About author:: CHEN Weijie(Corresponding author)， born in 1985， Ph.D.， associate professor. His research interests include semisupervised learning and support vector machine. SHAO Yuanhai， born in 1983， Ph.D.， associate professor. His research interests include data mining and nonlinear dimensionality reduction. LI Chunna， born in 1985， Ph.D.， lecturer. Her research interests include sparse learning and optimization theory. DENG Naiyang， born in 1937， Ph.D.， professor. His research interests include machine learning， support vector machine and optimization theory.

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	CHEN Weijie
	SHAO Yuanhai
	LI Chunna
	DENG Naiyang

Cite this article:

CHEN Weijie,SHAO Yuanhai,LI Chunna等. Semi-supervised Projection Twin Support Vector Machine via Manifold Regularization[J]. , 2016, 29(2): 97-107.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201602001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I2/97

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