Feature extractions for small sample size classification problem

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 3, 2007, Pages 756-764

  Kuo, Bor Chen ^a,b   Chang, Kuang Yu ^b  

^a IEEE   (Taiwan)

^b NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

Eigenvalue decomposition; Feature extraction; Genetic algorithm (GA); Regularization; Small sample size classification

Indexed keywords

EIGENVALUE DECOMPOSITION; REGULARIZATION; SMALL SAMPLE SIZE CLASSIFICATION;

EIGENVALUES AND EIGENFUNCTIONS; ELECTROMAGNETIC WAVE SCATTERING; FEATURE EXTRACTION; IMAGE PROCESSING; PROBLEM SOLVING;

CLASSIFICATION (OF INFORMATION);

EID: 33847762803     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2006.885074     Document Type: Article

References (30)

1. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ: Wiley, 2003.
2. C. A. Shah, P. Watanachaturaporn, M. K. Arora, and P. K. Varshney, "Some recent results on hyperspectral image classification," in Proc. IEEE Workshop Adv. Tech. Anal. Remotely Sensed Data, Oct. 27/28, 2003, pp. 346-353.
3. J. H. Friedman, "Regularized discriminant analysis," J. Amer. Stat. Assoc., vol. 84, no. 405, pp. 167-175, Mar. 1989.
4. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. (2004). A Practical Guide to Support Vector Classification. [Online]. Available: http://www.csie.ntu.edu. tw/~cjlin/libsvm
5. C.-C. Chang and C.-J. Lin. (2004). LIBSVM: A Library for Support Vector Machines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/ libsvm
6. K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA: Academic, 1990.
7. B.-C. Kuo and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1096-1105, May 2004.
8. _, "A covariance estimator for small sample size classification problems and its application to feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 4, pp. 814-819, Apr. 2002.
9. J. P. Hoffbeck and D. A. Landgrebe, "Covariance matrix estimation and classification with limited training data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 7, pp. 763-767, Jul. 1996.
10. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
11. C. Lee and D. A. Landgrebe, "Feature extraction based on decision boundaries," IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 4, pp. 388-400, Apr. 1993.
12. G. F. Hughes, "On the mean accuracy of statistical pattern recognition," IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
13. G.-S. Chen, L.-W. Ko, B.-C. Kuo, and S.-C. Shih, "A two-stage feature extraction for hyperspectral image data classification," in Proc. Int. Geosci. and Remote Sens. Symp., Sep. 20-24, 2004, pp. 1212-1215.
14. C. B. Moler and G. W. Stewart, "An algorithm for generalized matrix eigenvalue problems," SIAM J. Numer. Anal., vol. 10, no. 2, pp. 241-256, Apr. 1973.
15. C. E. Thomaz, D. F. Gillies, and R. Q. Feitosa, "A new covariance estimate for Bayesian classifiers in biometric recognition," IEEE Trans. Circuits Syst. Video Technol. - Special Issue on Image-and Video-Based Biometrics, vol. 14, no. 2, pp. 214-223, Feb. 2004.
16. C. E. Thomaz and D. F. Gillies, "A maximum uncertainty LDA-based approach for limited sample size problems-with application to face recognition," Dept. Computing, Imperial College, London, U.K., Tech. Rep. TR-2004-01, Jan. 2004.
17. G. H. Golub and C. Van Loan, Matrix Computations, 3rd ed. Baltimore, MD: The Johns Hopkins Univ. Press, 1996.
18. Z.-Q. Hong and J.-Y. Yang, "Optimal discriminant plane for a small number of samples," Pattern Recognit., vol. 24, no. 4, pp. 317-324, 1991.
19. H. Yu and J. Yang, "A direct LDA algorithm for high dimensional data - with application to face recognition," Pattern Recogn., vol. 34, no. 10, pp. 2067-2070, Oct. 2001.
20. P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The FERET database and evaluation procedure for face recognition algorithms," Image Vis. Comput. J., vol. 16, no. 5, pp. 295-306, 1998.
21. C. L. Wilson, G. T. Candela, P. J. Grother, C. I. Watson, and R. A. Wilkinson, "Massively parallel neural network fingerprint classification system," Nat. Inst. Standards Technol., Gaithersburg, MD, Tech. Rep. NISTIR 4880, 1992.
22. L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu, "A new LDA-based face recognition system which can solve the small sample size problem," Pattern Recognit., vol. 33, no. 10, pp. 1713-1726, 2000.
23. E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, LAPACK User's Guide, 3rd ed. Philadelphia, PA: SIAM, 1999. [Online]. Available: http://www.netlib.org/lapack/lug/lapack_lug.html
24. P. Howland and H. Park, "An optimization criterion for generalized discriminant analysis on undersampled problems," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 982-994, Aug. 2004.
25. J. Ye, R. Janardan, C. H. Park, and H. Park, "Generalizing discriminant analysis using the generalized singular value decomposition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 995-1006, Aug. 2004.
26. D. S. Watkins, Infinite Eigenvalues and the QZ Algorithm, 1999. (SCHEME=Freetext) Technische Universitaet Chemnitz, SFB 393, SFB393-Preprint 99-23. [Online]. Available: http://www.mathematik.tu-chemnitz.de/preprint/1999/ SFB393_23.html
27. S. Tadjudin and D. A. Landgrebe, "Covariance estimation with limited training samples," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 4, pp. 2113-2118, Jul. 1999.
28. C. Houck, J. Joines, and M. Kayin "A genetic algorithm for function optimization: A matlab implementation," North Carolina State Univ., Raleigh, NC, NCSU-IE TR 95-09, 1995. [Online], Available: http://www.ie.ncsu. edu/mirage/GAToolBox/gaot/
29. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer-Verlag, 1994. AT Series.
30. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press, 1994. AI Series.