A comparative study of linear and nonlinear feature extraction methods

Author

Park, Cheong Hee ; Park, Haesun ; Pardalos, Panos

Author_Institution

Dept. of Comput. Sci. & Eng., Minnesota Univ., Minneapolis, MN, USA

fYear

2004

fDate

1-4 Nov. 2004

Firstpage

495

Lastpage

498

Abstract

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.

Keywords

feature extraction; matrix algebra; computational complexity; generalized LDA algorithm; kernel method; linear discriminant analysis; linear feature extraction; nonlinear discriminant analysis; nonlinear feature extraction; Algorithm design and analysis; Chromium; Computational complexity; Eigenvalues and eigenfunctions; Feature extraction; Kernel; Linear discriminant analysis; Performance analysis; Scattering; Singular value decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Print_ISBN

0-7695-2142-8

Type

conf

DOI

10.1109/ICDM.2004.10066

Filename

1410344

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2851388