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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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