Nonlinear Discriminant Analysis Using Kernel Functions and the Generalized Singular Value Decomposition

@article{Park2005NonlinearDA,
  title={Nonlinear Discriminant Analysis Using Kernel Functions and the Generalized Singular Value Decomposition},
  author={Cheong Hee Park and Haesun Park},
  journal={SIAM J. Matrix Analysis Applications},
  year={2005},
  volume={27},
  pages={87-102}
}
Linear discriminant analysis (LDA) has been widely used for linear dimension reduction. However, LDA has limitations in that one of the scatter matrices is required to be nonsingular and the nonlinearly clustered structure is not easily captured. In order to overcome the problems caused by the singularity of the scatter matrices, a generalization of LDA based on the generalized singular value decomposition (GSVD) was recently developed. In this paper, we propose a nonlinear discriminant… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 19 REFERENCES

Fisher discriminant analysis with kernels

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K.R. Mullers
  • Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)
  • 1999
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Generalized Discriminant Analysis Using a Kernel Approach

  • Neural Computation
  • 2000
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Pattern classification and scene analysis

  • A Wiley-Interscience publication
  • 1973
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

A comparison of generalized lda algorithms for undersampled problems

C. H. PARK, H. PARK
  • Technical Reports 03-048, Department of Computer Science and Engineering, University of Minnesota, Twin NONLINEAR DISCRIMINANT ANALYSIS USING KERNEL FUNCTIONS AND THE GSVD 13 Cities
  • 2003
VIEW 1 EXCERPT

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