关键词:特征降维; 支持向量鉴别分析; 支持向量数据描述; 支持向量描述鉴别分析; 人脸识别
Support vector description discriminant analysis andits application to face recognition
CHEN Chang-jun, ZHAN Yong-zhao, WEN Chuan-jun
(School of Computer Science & Communication Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China)
Abstract:Dimension reduction of data is one of the most important components for pattern recognition. SVDA projected data according to the normal direction of SVM’s optimal separating hyperplane. SVDA could overcome the shortcomings of that traditional dimension reduction methods always assumes data meets Gaussian distribution. But the differences of some classes in the same side of SVM hyperplane could not be reflected by normal direction projection. This paper proposed a new dimension reduction method named as support vector description discriminant analysis. Obtained class information through SVM hyperplane and extracted projection distances through SVDD hypersphere projection normal, set the combination of class information and projection distance of sample data as corresponding feature component. And applied the algorithm to the feature extraction in face recognition. The results show the effectiveness of this algorithm. ......