Kernel Pooled Local Subspaces for Classification

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: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results demonstrate the effectiveness and performance superiority of the kernel pooled subspace method over competing methods such as PCA and KPCA in some classification problems.

Original languageEnglish
Title of host publication2003 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003
PublisherIEEE Computer Society
ISBN (Electronic)0769519008
DOIs
StatePublished - 1 Jan 2003
EventConference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003 - Madison, United States
Duration: 16 Jun 200322 Jun 2003

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume6
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

OtherConference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003
CountryUnited States
CityMadison
Period16/06/0322/06/03

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  • Cite this

    Zhang, P., Peng, J., & Domeniconi, C. (2003). Kernel Pooled Local Subspaces for Classification. In 2003 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003 [4624324] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 6). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2003.10060