KPCA plus LDA a complete kernel Fisher discriminant framework for feature extraction and recognition

KPCA plus LDA : a complete kernel Fisher discriminant framework for feature extraction and recognition

This paper examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 27(2005), 2 vom: 18. Feb., Seite 230-44
1. Verfasser: Yang, Jian (VerfasserIn)
Weitere Verfasser: Frangi, Alejandro F, Yang, Jing-Yu, Zhang, David, Jin, Zhong
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study