Class label versus sample label-based CCA

Applied Mathematics and Computation

Volumn 185, Issue 1, 2007, Pages 272-283

  Sun, Tingkai ^a   Chen, Songcan ^a  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

Canonical correlation analysis (CCA); Class label encoding; Feature extraction; Separability between classes

Indexed keywords

FEATURE EXTRACTION; IMAGE CODING; PROBLEM SOLVING;

CANONICAL CORRELATION ANALYSIS (CCA); CLASS LABEL ENCODING; LINEAR DISCRIMINANT ANALYSIS (LDA); SEPARABILITY BETWEEN CLASSES;

CORRELATION METHODS;

EID: 33846898949     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2006.06.103     Document Type: Article

References (18)

1. Duda R.O., Hart P.E., and Stock D.G. Pattern Classification. second ed. (2001), John Wiley and Sons, New York
2. Hotelling H. Relations between two sets of variates. Biometrika 28 (1936) 321-377
3. Hardoon D.R., Szedmak S., and Shawe-Taylor J. Canonical correlation analysis: An overview with application to learning method. Neural Computation 16 (2004) 2639-2664
4. Marco Loog, Bram van Ginneken, R.P.W. Duin, Dimensionality reduction by canonical contextual correlation projections, in: Proceedings of the eighth European Conference on Computer Vision, May 2004, pp. 562-573.
5. Y. Hel-Or, The canonical correlations of color images and their use for demosaicing, HP Labs Technical Report, HPL-2003-164 (R.1), 2004.
6. Melzer T., Reiter M., and Bischof H. Appearance models based on kernel canonical correlation analysis. Pattern Recognition 36 (2003) 1961-1971
7. T.V. Gestel, J.A.K. Suykens, J. De Brabanter, B. De Moor, J. Vandewalle, Kernel canonical correlation analysis and least squares support vector machines, in: Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001) 2001, pp. 384-389.
8. Horikawa Y. Use of Autocorrelation Kernels. Kernel Canonical Correlation Analysis for Texture Classification, ICONIP 2004. LNCS 3316 (2004), Springer-Verlag, Berlin 1235-1240
9. Baek J., and Kim M. Face recognition using partial least squares components. Pattern Recognition 37 (2004) 303-1306
10. Barker M., and Rayens W. Partial least squares for discrimination. Journal of Chemometrics 17 (2003) 166-173
11. B. Johansson, On classification: simultaneously reducing dimensionality and finding automatic representation using canonical correlation, Technical report LiTH-ISY-R-2375, ISSN 1400-3902, Linköping University, 2001.
12. M. Borga, Canonical correlation: A tutorial. Available online from: , 1999.
13. Bishop C.M. Neural Network for Pattern Recognition (1995), Clarendon Press, Oxford
14. Cover T.M., and Hart P.E. Nearest neighbor pattern classification. IEEE Transactions of Information Theory 13 (1967) 21-27
15. Keller J.M., Gray M.R., and Givens J.A. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems Man and Cybernetics 15 (1985) 580-585
16. Chen S.C., Liu J., and Zhou Z.H. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition 37 (2004) 1553-1555
17. Shuicheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Graph embedding: A general framework for dimensionality reduction, in: Proceeding of IEEE CVPR'05, 2005.
18. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and other Kernel-based Learning Methods (2000), Cambridge University Press