Dimensionality Reduction using Kernel Pooled Local Discriminant Information
Document Type
Conference Proceeding
Publication Date
12-1-2003
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.
Montclair State University Digital Commons Citation
Zhang, Peng; Peng, Jing; and Domeniconi, Carlotta, "Dimensionality Reduction using Kernel Pooled Local Discriminant Information" (2003). Department of Computer Science Faculty Scholarship and Creative Works. 223.
https://digitalcommons.montclair.edu/compusci-facpubs/223