A comparison of sparse kernel principal component analysis methods

Author

Zhen Kun Gon ; Feng, JunKang ; Fyfe, Colin

Author_Institution

Applied Comput. Intelligence Res. Unit, Paisley Univ., UK

Volume

1

fYear

2000

fDate

2000

Firstpage

309

Abstract

Presents a comparative study of a group of methods based on kernels which attempt to identify only the most significant cases with which to create the nonlinear feature space. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g. regression or classification). We use extensions of kernel principal component analysis to identify the optimal cases to create a sparse representation in feature space. The efficiency of the kernel models is compared on an oceanographic problem

Keywords

covariance matrices; eigenvalues and eigenfunctions; geophysics computing; learning automata; oceanography; principal component analysis; sparse matrices; classification; data points; efficiency; nonlinear feature space; oceanographic problem; optimal cases; regression; significant cases; sparse kernel principal component analysis methods; sparse representation; support vector machines; Computational intelligence; Context modeling; Covariance matrix; Eigenvalues and eigenfunctions; Intelligent systems; Kernel; Principal component analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885818

Filename

885818