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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 27; číslo 2; s. 230 - 244
Hlavní autoři:	Jian Yang, Frangi, A.F., Jing-Yu Yang, David Zhang, Zhong Jin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.02.2005 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Artificial Intelligence Computer science; control theory; systems Computer Simulation Discriminant Analysis Exact sciences and technology Face - anatomy & histology Face recognition Feature extraction Fisher linear discriminant analysis (LDA or FLD) Handwriting Handwriting recognition handwritten digit recognition Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Index Terms- Kernel-based methods Information Storage and Retrieval - methods Intelligence Kernel Kernels Linear discriminant analysis Machine learning Machine learning algorithms Matrix decomposition Models, Biological Models, Statistical Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Principal component analysis principal component analysis (PCA) Recognition Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Spatial databases subspace methods Subtraction Technique Fisher linear discriminant analysis (LDA or FLD) Face recognition feature extraction Kernel-based methods principal component analysis (PCA) Subspace method handwritten digit recognition subspace methods Handwritten character recognition machine learning Pattern extraction Principal component analysis
ISSN:	0162-8828, 1939-3539
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