Fisher's Linear Discriminant Analysis With Space-Folding Operations

Chin-Chun Chang

2023IEEE Transactions on Pattern Analysis and Machine Intelligence16 citationsDOI

Abstract

Fisher's linear discriminant analysis (LDA) is an easy-to-use supervised dimensionality reduction method. However, LDA may be ineffective against complicated class distributions. It is well-known that deep feedforward neural networks with rectified linear units as activation functions can map many input neighborhoods to similar outputs by a succession of space-folding operations. This short paper shows that the space-folding operation can reveal to LDA classification information in the subspace where LDA cannot find any. A composition of LDA with the space-folding operation can find classification information more than LDA can do. End-to-end fine-tuning can improve that composition further. Experimental results on artificial and open data sets have shown the feasibility of the proposed approach.

Topics & Concepts

Linear discriminant analysisPattern recognition (psychology)Subspace topologyDimensionality reductionArtificial intelligenceFolding (DSP implementation)Kernel Fisher discriminant analysisComputer scienceCurse of dimensionalityDiscriminantPrincipal component analysisMathematicsEngineeringElectrical engineeringFacial recognition systemFace and Expression RecognitionNeural Networks and ApplicationsMachine Learning and Data Classification