Improved support vector classification using PCA and ICA feature space modification

Pattern Recognition

Volumn 37, Issue 6, 2004, Pages 1117-1129

  Fortuna, Jeff ^a   Capson, David ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Independent component analysis; Principal component analysis; Support vector machine

Indexed keywords

DATABASE SYSTEMS; ERROR ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

FEATURE SELECTION; FEATURE VECTORS; OPTIMAL CLASSIFICATION; SUPPORT VECTOR CLASSIFICATION;

FEATURE EXTRACTION;

PATTERN RECOGNITION;

EID: 2142813788     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2003.11.009     Document Type: Article

References (18)

1. M.A. Turk, A.P. Pentland, Face recognition using eigenfaces, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Maui, HI, USA, June 1991, pp. 586-591.
2. Murase H. Nayar S.K. Visual learning and recognition of 3D objects from appearance Int. J. Comput. Vision 14 1 1995 5-24
3. J. Fortuna, D.C. Schuurman, D. Capson, A comparison of PCA and ICA for object recognition under varying illumination, Proceedings of the International Conference on Pattern Recognition (ICPR), Vol. 3, Que., Canada, August, 2002, pp. 11-15.
4. Nayar S.K. Nene S.A. Murase H. Subspace methods for robot vision IEEE Trans. Robotics Automation 12 5 1996 750-758
5. J. Fortuna, D. Capson, ICA for position and pose measurement from images with occlusion, Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 4, Orlando, FL, May 2002, pp. 3604-3607.
6. Field D.J. Relations between the statistics of natural images and the response properties of cortical cells J. Opt. Soc. Am. 4 12 1987 2379-2394
7. Bell A. Sejnowski T. The independent components of natural scenes are edge filters Vision Res. 37 23 1997 3327-3338
8. M. Bartlett, T. Sejnowski, Independent components of face images: A representation for face recognition, Proceedings of the Fourth Annual Joint Symposium on Neural Computation, Pasadena, CA, 1997.
9. Pudil P. Novovicova J. Kittler J. Floating search methods in feature selection Pattern Recognition Lett. 15 11 1994 1119-1125
10. K. Messer, D. De Ridder, J. Kittler, Adaptive texture representation methods for automatic target recognition, BMVC99 Proceedings of the 10th British Machine Vision Conference, Vol. 2, Nottingham University, Nottingham, UK, 1999, pp. 443-452.
11. Y. Qi, D. Doermann, D. DeMenthon, Hybrid independent component analysis and support vector machine learning scheme for face detection, Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, UT, May 2001, pp. 3327-3338.
12. X. Zhang, V. Ramani, Z. Long, Y. Zeng, A. Ganapathiraju, J. Picone, Scenic beauty estimation using independent component analysis and support vector machines, Proceedings of the IEEE Southeastcon, Lexington, KT, March, 1999, pp. 274-277.
13. Bell A. Sejnowski T. An information-maximization approach to blind separation and blind deconvolution Neural Comput. 7 1995 1129-1159
14. Hyvärinen A. Oja E. A fast fixed-point algorithm for independent component analysis Neural Comput. 9 7 1997 1483-1492
15. Cristianini N. Shawe-Taylor J. An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge, UK
16. Tax D. Duin R. Support vector domain description Pattern Recognition Lett. 20 1999 1119-1125
17. Georghiades A.S. Belhumeur P.N. Kriegman D.J. From few to many: Illumination cone models for face recognition under variable lighting and pose IEEE Trans. Pattern Anal. Mach. Intell. 23 6 2001 643-660
18. H. Murase, S.K. Nayar, Dimensionality of illumination manifolds in eigenspace, CUCS-021-94 Technical Report, Department of Computer Science, Columbia University, August 1994.