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A comparative study of linear and nonlinear feature extraction methods

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3 Author(s)
Cheong Hee Park ; Dept. of Comput. Sci. & Eng., Minnesota Univ., Minneapolis, MN, USA ; Haesun Park ; Pardalos, P.

This paper presents theoretical relationships among several generalized LDA algorithms and proposes computationally efficient approaches for them utilizing the relationships. Generalized LDA algorithms are extended nonlinearly by kernel methods resulting in nonlinear discriminant analysis. Performances and computational complexities of these linear and nonlinear discriminant analysis algorithms are compared.

Published in:

Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on

Date of Conference:

1-4 Nov. 2004