A Novel Supervised Feature Extraction Method by KPCA and Its Application
Based on kernel principal component analysis (KPCA), a novel supervised feature extraction method is presented in this paper. The new method has two traits: one is to sufficiently utilize a given class label of training kernel sample in feature extraction and the other is to still follow the same mathematical formulation as KPCA, so the novel method is named as supervised kernel principal component analysis (SKPCA). Besides, in order to further improve recognition rate, the paper presents a new classification strategy based on fuse of two kinds of feature vector. The experimental results on Olivetti Research Laboratory (ORL) face database and YALE face database show that the new method is better than KPCA in terms of recognition rate.
Yongzhi Li Jingyu Yang Guangming He
School of Information Science & Technology, Nanjing Forestry University, Nanjing 210037 P.R. China;D Department of Computer Science, Nanjing Univ. of Science & Technology, Nanjing 210094 P.R.China
国际会议
南宁
英文
2007-07-20（万方平台首次上网日期，不代表论文的发表时间）