An Iterative Algorithm For Klda Classifier release_xlzc2wksrjd2ldkjiqmj6ebbzy

by D.N. Zheng, J.X. Wang, Y.N. Zhao, Z.H. Yang

Published by Zenodo.

2007  

Abstract

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.
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Date   2007-04-27
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