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| Semi-Supervised Proximal Support Vector Machine via Generalized Eigenvalues |
| YANG Xu-Bing1,2, PAN Zhi-Song3, CHEN Song-Can1 |
1.College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing 210016
2.College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037
3.Institute of Command Automation, PLA University of Science Technology, Nanjing 210007 |
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Abstract A binary classifier, proximal support vector machine via generalized eigenvalues (GEPSVM), has been proposed recently. In this paper, with the characteristics of plane classifiers and manifold learning, an effective semi-supervised algorithm SemiGEPSVM is proposed. It keeps the performance of handling XOR problems and is suitable for more challenges, even with only one labeled sample per class. While the number of labeled samples is not satisfactory to generate plane, k-nearest neighbor is used to select the unlabelled samples. Otherwise, the proposed sample selection method with plane characteristics is adopted. Furthermore, it is proved that the proposed selection method is global optimization. And the experimental results of SemiGEPSVM are verified on one toy problem and some benchmark datasets.
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Received: 29 April 2008
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2009, Vol. 22 
