Supervised Kernel Construction for Unsupervised PCA on Face Recognition
This paper aims to establish a novel framework for highperformance Mercer kernel construction.Based on a given kernel matrix incorporated the class label information,a nonlinear mapping is firstly generated and well-defined on the training samples.The partial data-defined mapping can be extended and well-defined on the entire pattern space by means of interpolatory technology.The analytic expression of the nonlinear mapping is then obtained.It theoretically shows that the function K(x,y),created by the inner product of the nonlinear mapping,is a supervised Mercer kernel function.Our supervised kernel is successfully applied to unsupervised principal component analysis (PCA) method for face recognition.Two face databases,namely ORL and FERET databases,are selected for evaluations.Compared with KPCA with RBF kernel (RBF-PCA) method,experimental results demonstrate that KPCA with our supervised kernel (SK-PCA) has superior performance.
Face Recognition Supervised Mercer Kernel Kernel PCA
Yang Zhao Wen-Sheng Chen Binbin Pan Bo Chen
College of Mathematics and Computational Science,Shenzhen University Shenzhen Key Laboratory of Media Security Shenzhan,518060,China
国际会议
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)
长沙
英文
351-359
2014-11-01（万方平台首次上网日期，不代表论文的发表时间）