Manifold Regularized Proximal Support Vector Machine via Generalized Eigenvalue

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/).

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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  -