A Comparative Study of Linear and Nonlinear Feature Extraction Methods

Authors:
Cheong Hee Park;Haesun Park;Panos Pardalos
Affiliations:
University of Minnesota, Minneapolis;University of Minnesota, Minneapolis;University of Florida, Gainesville
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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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.