Kernel discriminant analysis with weighted schemes and its application to face recognition

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.