Fisher discriminant analysis with kernels

Abstract

A non-linear classification technique based on Fisher's discriminant is proposed. Main ingredient is the kernel trick which allows to efficiently compute the linear 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.

Keywords

Kernel Fisher discriminant analysisFisher kernelLinear discriminant analysisKernel (algebra)Pattern recognition (psychology)Optimal discriminant analysisArtificial intelligenceMultiple discriminant analysisDiscriminant function analysisMathematicsDiscriminantFeature vectorFeature (linguistics)ComputationComputer scienceAlgorithmStatisticsCombinatorics

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Publication Info

Year: 2003
Type: article
Pages: 41-48
Citations: 2657
Access: Closed

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APA Style

                            
                                    Mika Sirén, 
                                
                                    Gunnar Rätsch, 
                                
                                    Jason Weston
                                
                                et al.
                            
                            (2003). 
                            Fisher discriminant analysis with kernels. 
                            
                            , 41-48.
                            https://doi.org/10.1109/nnsp.1999.788121

Identifiers

DOI: 10.1109/nnsp.1999.788121