A Comparative Study of Linear and Nonlinear Feature Extraction Methods
Cheong Hee Park, Haesun Park, Pãnos M. Pardalos · 2005
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.