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2003
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Dimensionality Reduction Using Kernel Pooled Local Discriminant Information

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Dimensionality Reduction Using Kernel Pooled Local Discriminant Information
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.
Peng Zhang, Jing Peng, Carlotta Domeniconi
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICDM
Authors Peng Zhang, Jing Peng, Carlotta Domeniconi
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