Kernel subclass discriminant analysis

Neurocomputing

Volumn 71, Issue 1-3, 2007, Pages 455-458

  Chen, Bo ^a   Yuan, Li ^a   Liu, Hongwei ^a   Bao, Zheng ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Feature space; Kernel linear discriminant analysis (KLDA); Kernel methods; Linear discriminant analysis (LDA); Subclass discriminant analysis (SDA)

Indexed keywords

CLUSTER ANALYSIS; COVARIANCE MATRIX; MULTIVARIANT ANALYSIS; NORMAL DISTRIBUTION;

FEATURE SPACE; KERNEL LINEAR DISCRIMINANT ANALYSIS; SUBCLASS DISCRIMINANT ANALYSIS;

DISCRIMINANT ANALYSIS;

ARTICLE; CLASSIFICATION; CLUSTER ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; DISCRIMINANT ANALYSIS; INTERMETHOD COMPARISON; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; NONLINEAR SYSTEM; PRIORITY JOURNAL;

EID: 35648999420     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.006     Document Type: Article

References (12)

1. Baudat G., and Anouar F. Generalized discriminant analysis using a kernel approach. Neural Comput. 12 (2000) 2385-2404
2. C. Blake, E. Keogh, C.J. Merz. 1998, UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine, CA (Online). Available from 〈http://www.ics.uci.edu/mlearn〉.
3. B. Chen, H. Liu, Z. Bao, A kernel optimization method based on the localized kernel Fisher criterion, Pattern Recognition (2007), doi: 10.1016/j.patcog.2007.08.009.
4. Fukunaga K. Introduction to Statistical Pattern Recognition (1990), Academic Press Inc., Boston
5. Kim T.K., and Kittler J. Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Trans. PAMI 27 3 (2005) 318-327
6. B. Leibe, B. Schiele, Analyzing appearance and contour based methods for object categorization, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 2003.
7. Lu J., Plataniotis K.N., and Venetsanopoulos A.N. Face recognition using kernel discriminant analysis algorithms. IEEE Trans. Neural Networks 14 1 (2003) 117-126
8. Martinez A.M., and Zhu M. Where are linear feature extraction methods applicable?. IEEE Trans. PAMI 27 12 (2005) 1934-1944
9. S. Roweis, Finding the First Few Eigenvectors in a Large Space, Available from 〈http://www.cs.toronto.edu/∼roweis/〉 .
10. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, Cambridge
11. Yang J., Frangi A.F., Yang J., Zhang D., and Jin Z. KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. PAMI 27 2 (2005) 230-244
12. Zhu M., and Martinez A.M. Subclass discriminant analysis. IEEE Trans. PAMI 28 8 (2006) 1274-1286