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.