Dimensionality reduction using kernel pooled local discriminant information

Peng Zhang, Jing Peng, Carlotta Domeniconi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: generalized Fisher discriminant analysis (GDA) and kernel principal components analysis (KPCA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results demonstrate the efficacy of the kernel pooled local subspace method and the potential for substantial improvements over competing methods such as KPCA in some classification problems.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages701-704
Number of pages4
StatePublished - 1 Dec 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: 19 Nov 200322 Nov 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period19/11/0322/11/03

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