Improved support vectors for classification through preserving neighborhood geometric structure constraint

作者：Wang, Xuchu^*; Niu, Yanmin

来源：Optical Engineering, 2011, 50(8): 087202.

DOI：10.1117/1.3610982

• pattern classification
• support vector machine
• manifold learning
• locally linear embedding
• neighborhood preserving embedding
• small sample size problem

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.

• 出版日期2011-8
• 单位重庆大学; 重庆师范大学