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- Title
Improved support vectors for classification through preserving neighborhood geometric structure constraint.
- Authors
Wang, Xuchu; Niu, Yanmin
- 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.
- Subjects
SUPPORT vector machines; PATTERN recognition systems; COMPUTER vision; IMAGE processing; IMAGING systems
- Publication
Optical Engineering, 2011, Vol 50, Issue 8, p087202
- ISSN
0091-3286
- Publication type
Article
- DOI
10.1117/1.3610982