Title

Kernel Pooled Local Subspaces for Classification

Document Type

Article

Publication Date

6-1-2005

Abstract

We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results using several data sets demonstrate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems.

DOI

10.1109/TSMCB.2005.846641

This document is currently not available here.

Share

COinS