Fisher Discriminant Analysis with Kernels

Mika, S.,Ratsch, G.,Weston, J.,Scholkopf, B.

Madison, WI（1999）

GMD FIRST

被引用3927|浏览87

摘要

A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.

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关键词

Bayes methods,decision theory,feature extraction,learning (artificial intelligence),neural nets,pattern classification,Fisher discriminant analysis,feature space,input space,kernels,linear classification,nonlinear classification technique,nonlinear decision function