Abstract
Twin support vector machines are a recently proposed learning method for pattern classification. They learn two hyperplanes rather than one as in usual support vector machines and often bring performance improvements. Semi-supervised learning has attracted great attention in machine learning in the last decade. Laplacian support vector machines and Laplacian twin support vector machines have been proposed in the semi-supervised learning framework. In this paper, inspired by the recent success of multi-view learning we propose multi-view Laplacian twin support vector machines, whose dual optimization problems are quadratic programming problems. We further extend them to kernel multi-view Laplacian twin support vector machines. Experimental results demonstrate that our proposed methods are effective.
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Notes
http://archive.ics.uci.edu/ml/datasets/Ionosphere
We do not detail the MvTSVMs here. They are supervised extensions of TSVMs to multi-view learning.
https://archive.ics.uci.edu/ml/datasets/Multiple+Features
http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Projects 61370175 and 61075005, and Shanghai Knowledge Service Platform Project (No. ZF1213).
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Xie, X., Sun, S. Multi-view Laplacian twin support vector machines. Appl Intell 41, 1059–1068 (2014). https://doi.org/10.1007/s10489-014-0563-8
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DOI: https://doi.org/10.1007/s10489-014-0563-8