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支持向量描述鉴别分析及在人脸识别中的应用陈长军 詹永照 文传军


摘 要:数据降维是模式识别的重要组成部分。支持向量鉴别分析(support vector discriminant analysis, SVDA)依最优超平面法线方向投影对数据进行降维,克服了传统方法中假设数据满足高斯分布时,导致无法反映超平面单侧中多类数据间投影距离差异并影响了算法有效性的缺点。提出一种支持向量描述鉴别分析(support vector description discriminant analysis, SVDDA)算法,首先利用支持向量机最优超平面获取样本的类属信息,然后通过SVDD的超球面法线作为投影轴取得样本的投影距离,取两信息的组合作为样本的特征映射。算法利用SVDD的一类紧致超球特性,弥补支持向量鉴别分析的不足。通过人脸识别实验,验证了该算法的有效性。
  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
  中图分类号:TP391.41
  文献标志码:A
  
  文章编号:1001-3695(2010)02-0488-03
  doi:10.3969/j.issn.1001-3695.2010.02.022
  
  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. ......
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