Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Discriminant Analysis for Recognition of Human Face Images (Invited Paper)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
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We study the use of kernel subspace methods for learninglow-dimensional representations for classification. We proposea kernel pooled local discriminant subspace methodand compare it against several competing techniques: generalizedFisher discriminant analysis (GDA) and kernelprincipal components analysis (KPCA) in classificationproblems. We evaluate the classification performance ofthe nearest-neighbor rule with each subspace representation.The experimental results demonstrate the efficacy ofthe kernel pooled local subspace method and the potentialfor substantial improvements over competing methods suchas KPCA in some classification problems.