International Journal of Computational Intelligence Systems

Volume 9, Issue 6, December 2016, Pages 1041 - 1054

Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue

Authors
Jun Liang*, Fei-yun Zhang, Xiao-xia Xiong, Xiao-bo Chen, Long Chen, Guo-hui Lan
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, 212013, P. R. China
* Corresponding author. E-mail:liangjun@ujs.edu.cn.
Corresponding Author
Jun Liang
Received 8 April 2014, Accepted 11 April 2016, Available Online 1 December 2016.
DOI
10.1080/18756891.2016.1256570How to use a DOI?
Keywords
Support vector machines; Generalized eigenvalues; Locality preserving projections; Manifold regularization
Abstract

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.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 6
Pages
1041 - 1054
Publication Date
2016/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1256570How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Jun Liang
AU  - Fei-yun Zhang
AU  - Xiao-xia Xiong
AU  - Xiao-bo Chen
AU  - Long Chen
AU  - Guo-hui Lan
PY  - 2016
DA  - 2016/12/01
TI  - Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue
JO  - International Journal of Computational Intelligence Systems
SP  - 1041
EP  - 1054
VL  - 9
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1256570
DO  - 10.1080/18756891.2016.1256570
ID  - Liang2016
ER  -