The use of kernel principal component analysis to model data distributions

DOI10.1016/S0031-3203(02)00051-1zbMath1010.68206MaRDI QIDQ1856666

C. J. Taylor, Carole J. Twining

Publication date: 11 February 2003

Published in: Pattern Recognition (Search for Journal in Brave)

zbMATH Keywords

feature extractiondensity estimationsupport vector machinesprincipal componentskernel PCAkernel functions

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10)

Related Items (6)

Handwritten Bangla numeral recognition system and its application to postal automation ⋮ A geometric approach to non-parametric density estimation ⋮ Efficiently updating and tracking the dominant kernel principal components ⋮ Nyström-based approximate kernel subspace learning ⋮ Non-negativity constraints on the pre-image for pattern recognition with kernel machines ⋮ On selection of kernel parametes in relevance vector machines for hydrologic applications

Uses Software

• UCI-ml

Cites Work

• Kernel PCA for feature extraction and de-noising in nonlinear regression
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