Laplacian twin support vector machine for semi-supervised classification

Authors:
Zhiquan Qi;Yingjie Tian;Yong Shi
Affiliations:
Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China and College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE ...
Venue:
Neural Networks
Year:
2012

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Abstract

Semi-supervised learning has attracted a great deal of attention in machine learning and data mining. In this paper, we have proposed a novel Laplacian Twin Support Vector Machine (called Lap-TSVM) for the semi-supervised classification problem, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier and be a useful extension of TSVM. Furthermore, by choosing appropriate parameters, Lap-TSVM degenerates to either TSVM or TBSVM. All experiments on synthetic and real data sets show that the Lap-TSVM's classifier combined by two nonparallel hyperplanes is superior to Lap-SVM and TSVM in both classification accuracy and computation time.