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Fisher's Linear Discriminant Analysis With Space-Folding Operations

Chang, Chin-Chun

IEEE transactions on pattern analysis and machine intelligence, 2023-07, Vol.45 (7), p.9233-9240 [Periódico revisado por pares]

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Título:
Fisher's Linear Discriminant Analysis With Space-Folding Operations
Autor: Chang, Chin-Chun
Assuntos: Covariance matrices ; Eigenvalues and eigenfunctions ; Feature extraction ; Feedforward neural networks ; Fisher's linear discriminant analysis ; Kernel ; Linear discriminant analysis ; rectified linear units ; space-folding operations ; Training
É parte de: IEEE transactions on pattern analysis and machine intelligence, 2023-07, Vol.45 (7), p.9233-9240
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
Descrição: 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.
Editor: United States: IEEE
Idioma: Inglês

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