Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue
Autor: | Xiaobo Chen, Xiaoxia Xiong, Zhang Feiyun, Long Chen, Guo-hui Lan, Jun Liang |
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Jazyk: | angličtina |
Rok vydání: | 2016 |
Předmět: |
0209 industrial biotechnology
Manifold alignment Mathematical optimization Support vector machines General Computer Science Plane (geometry) 02 engineering and technology Regularization (mathematics) Manifold lcsh:QA75.5-76.95 Support vector machine Computational Mathematics 020901 industrial engineering & automation Kernel method Manifold regularization Test set 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Locality preserving projections Generalized eigenvalues Algorithm Eigenvalues and eigenvectors Mathematics |
Zdroj: | International Journal of Computational Intelligence Systems, Vol 9, Iss 6 (2016) International Journal of Computational Intelligence Systems, Vol 9, Iss 6 (2017) |
ISSN: | 1875-6883 |
Popis: | Proximal support vector machine via generalized eigenvalue (GEPSVM) is a recently proposed binary classification technique which aims to seek two nonparallel planes so that each one is closest to one of the two datasets while furthest away from the other. In this paper, we proposed a novel method called Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue (MRGEPSVM), which incorporates local geometry information within each class into GEPSVM by regularization technique. Each plane is required to fit each dataset as close as possible and preserve the intrinsic geometric structure of each class via manifold regularization. MRGEPSVM is also extended to the nonlinear case by kernel trick. The effectiveness of the method is demonstrated by tests on some examples as well as on a number of public data sets. These examples show the advantages of the proposed approach in both computation speed and test set correctness. |
Databáze: | OpenAIRE |
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