KERNEL DISCRIMINANT ANALYSIS WITH WEIGHTED SCHEMES AND ITS APPLICATION TO FACE RECOGNITION

摘要：

Kernel discriminant analysis (KDA) is a widely used tool for feature extraction. But for high-dimensional multi-class tasks such as face recognition, traditional KDA algorithms have the limitation that the Fisher criterion is nonoptimal with respect to classification rate. Moreover, they suffer from the small sample size problem. This paper presents a variant of KDA that deals with both of the shortcomings in an efficient and cost effective manner. Experiments on face recognition task show that the proposed method is superior to traditional KDA.

关键词： Feature Eztraction Kernel Discriminant Analysis (KDA) Small Sample Size Face Recognition

作者: DA-KE ZHOU ZHEN-MING TANG

作者单位: Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 448-453

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）