Lancaster EPrints (Lancaster University) · 2003 · 23 citations · 11 references
Open access
Image AnalysisMachine LearningData ScienceEngineeringPattern RecognitionKernel MethodReproducing Kernel MethodKernel Principal ComponentsMultilinear Subspace LearningGeneralized Hebbian AlgorithmComputer ScienceNonlinear Dimensionality ReductionMedical Image ComputingPrincipal Component AnalysisKernel Hebbian AlgorithmPrincipal Components
A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method and preliminary applications in image hyperresolution are presented. In addition, we discuss the extension of the method to the online learning of kernel principal components.
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From few to many: illumination cone models for face recognition under variable lighting and pose
Athinodoros S. Georghiades, Peter N. Belhumeur, David Kriegman · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2001 · 4.9K citations
Generative Appearance-based Method, Engineering, Biometrics +19
Fisher discriminant analysis with kernels
Gunnar Rätsch, Jason Weston, Bernhard Schölkopf et al. · 2003 · 2.7K citations
Fisher Discriminant Analysis, Engineering, Machine Learning +20