JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2011, Vol. 41 ›› Issue (1): 17-23.

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Comparison of KPCA transformation matrices with definite and indefinite kernels for high-dimensional data

CUI Yan, FAN Li-ya   

  1. School of Mathematical Sciences, Liaocheng University, Liaocheng 252059, China
  • Received:2010-04-20 Online:2011-02-16 Published:2010-04-20

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Abstract:

The transformation matrices in the first stage of two-stage dimension reduction KPCA (kernel principal component analysis)+LDA (linear discriminant analysis) influenced the classification results of data. For linear non-separated problems, the influence of the transformation matrices in the first stage of KPCA+LDA to the classification results with definite kernels and then with indefinite kernels was first studied. In addition, experiments were provided for analyzing and illustrating the results.
 

Key words: principal component analysis, linear discriminant analysis, definite kernel, indefinite kernel, dimension reduction transformation matrix

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