A Class-Information-Incorporated Kernel Principal Component Analysis Method
LI Yong-Zhi1,2, YANG Jing-Yu2, WU Song-Song2
1.School of Information Science and Technology, Nanjing Forestry University, Nanjing 2100372. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
Abstract A supervised feature extraction method based on kernel principal component analysis (KPCA) is presented. In feature extraction the class information of the training kernel sample is sufficiently utilized, and the simple mathematical formulation is employed which is similar to KPCA. Thus, the method is named as class-information-incorporated kernel principal component analysis (CIKPCA). Furthermore, a new classification strategy is presented by fusing two kinds of feature vectors to improve the recognition rate. The experimental results on three databases show that the proposed method is better than KPCA in terms of recognition rate, and it even outperforms KLDA.
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2008, Vol. 21 
