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Optical Engineering

Improved support vectors for classification through preserving neighborhood geometric structure constraint
Author(s): Xuchu Wang; Yanmin Niu
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Paper Abstract

The support vector machine (SVM) has become a popular classifier in pattern recognition, computer vision, and other fields. Traditional SVM may result in a nonrobust solution for classifying complex data because its separating hyperplane only reflects the marginal distance information of isolated support vectors, while discarding some useful class structural information. In this paper a new support vector classifier with neighborhood preserving constraint is proposed to enhance the support vectors by preserving the local geometric structure on the manifold of within-class samples. This structure can be represented as a weighted graph matrix and regulated by adding a preprocessing transform in standard SVM. Experimental results validate its effectiveness with comparison to related methods on several synthetic and real-world data sets and show its competence, especially for classifying high dimensional data in a small sample size case.

Paper Details

Date Published: 1 August 2011
PDF: 17 pages
Opt. Eng. 50(8) 087202 doi: 10.1117/1.3610982
Published in: Optical Engineering Volume 50, Issue 8
Show Author Affiliations
Xuchu Wang, Chongqing Univ. (China)
Yanmin Niu, Chongqing Normal Univ. (China)

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