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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2014

01-06-2014 | Original Article

Laplacian smooth twin support vector machine for semi-supervised classification

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2014

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Abstract

Laplacian twin support vector machine (Lap-TSVM) is a state-of-the-art nonparallel-planes semi-supervised classifier. It tries to exploit the geometrical information embedded in unlabeled data to boost its generalization ability. However, Lap-TSVM may endure heavy burden in training procedure since it needs to solve two quadratic programming problems (QPPs) with the matrix “inversion” operation. In order to enhance the performance of Lap-TSVM, this paper presents a new formulation of Lap-TSVM, termed as Lap-STSVM. Rather than solving two QPPs in dual space, firstly, we convert the primal constrained QPPs of Lap-TSVM into unconstrained minimization problems (UMPs). Afterwards, a smooth technique is introduced to make these UMPs twice differentiable. At last, a fast Newton–Armijo algorithm is designed to solve the UMPs in Lap-STSVM. Experimental evaluation on both artificial and real-world datasets demonstrate the benefits of the proposed approach.
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Footnotes
1
Matlab is available at http://​www.​mathworks.​com.
 
2
The UCI datasets are available at http://​archive.​ics.​uci.​edu/​ml.
 
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Metadata
Title
Laplacian smooth twin support vector machine for semi-supervised classification
Publication date
01-06-2014
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2014
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0183-3

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