PKPCA:A Nonlinear Principal Component Analysis Algorithm Integrating Priori Class Information
Xie Ying-chun · Journal of Circuits and Systems · 2003
The paper proposed a Priori Kernel Principal Component Analysis (PKPCA), which integrates between and within class variances into KPCA, and thus the classification performances can be enhanced. To get sparse sample library and reduce eigen-vector dimension, a new concept of reconstructing sample library and its corresponding algorithm are introduced and presented, respectively. Further, both of KPCA (Kernel Principal Component Analysis) and KFD (Kernel Fisher Discriminant) can be proved to be two special cases of PKPCA, and meanwhile PKPCA successfully avoids the disadvantage of KFD that can only get (numbers of class 1) eigen-vectors. Simulation results of two numerical function classes, as well as experiment results of a real world data set involving credit card, chronometer and diseases, show that the proposed algorithm is valid and the classification performance is satisfied.