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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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 27; číslo 2; s. 230 - 244
Hlavní autori:	Jian Yang, Frangi, A.F., Jing-Yu Yang, David Zhang, Zhong Jin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.02.2005 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Popis
Shrnutí:	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 on this framework, the authors propose a complete kernel Fisher discriminant analysis (CKFD) algorithm. CKFD can be used to carry out discriminant analysis in "double discriminant subspaces." The fact that, it can make full use of two kinds of discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Undefined-3
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2005.33