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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 27; H. 2; S. 230 - 244
Hauptverfasser: Jian Yang, Frangi, A.F., Jing-Yu Yang, David Zhang, Zhong Jin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.02.2005
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2005.33