An Iterative Algorithm For Klda Classifier
Dong Zheng, J. X. Wang, Yunfeng Zhao, Zi-Jiang Yang · Zenodo (CERN European Organization for Nuclear Research) · 2007
The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experiments show that it has a comparable performance with SVM.