Abstract
In this paper, a novel feature extraction method based on scatter difference criterion in hidden space is developed. Its main idea is that the original input space is first mapped into a hidden space through a kernel function, where the feature extraction is conducted using the difference of between-class scatter and within-class scatter as the discriminant criterion. Different from the existing kernel-based feature extraction methods, the kernel function used in the proposed one is not required to satisfy Mercer’s theorem so that they can be chosen from a wide range. It is more important that due to adoption of the scatter difference as the discriminant criterion for feature extraction, the proposed method essentially avoids the small size samples problem usually occurred in the kernel Fisher discriminant analysis. Finally, extensive experiments are performed on a subset of FERET face database. The experimental results indicate that the proposed method outperforms the traditional scatter difference discriminant analysis in recognition performance.
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References
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New york (1995)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.-R.: Fisher discriminant analysis with kernels. In: IEEE International Workshop on Neural Networks for Signal Processing IX, Madison (USA), pp. 41–48 (August 1999)
Mika, S., Smola, A.J., Schölkopf, B.: An improved training algorithm for kernel fisher discriminants. In: Jaakkola, T., Richardson, T. (eds.) Proceedings AISTATS 2001, San Francisco, CA, pp. 98–104 (2001)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.-R.: Constructing descriptive and discriminative non-linear features: Rayleigh coefficients in kernel feature spaces. IEEE Trans. Pattern Anal. Machine Intell. 25(5), 623–628 (2003)
Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 12(10), 2385–2404 (2000)
Yang, M.H.: Kernel Eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (RGR 2002), Washington D. C., pp. 215–220 (May 2002)
Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)
Song, F., Liu, S., Yang, J., et al.: Maximum scatter difference classifier and its application to text categorization. Computer Engineering 31(5), 890–896 (2005)
Yun-peng, C.: Matrix theory (in chinese). Northwest Industry University Press, Xi’an (1999)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1090–1104 (2000)
Phillips, P.J.: The Facial Recognition Technology (FERET) Database, http://www.itl.nist.gov/iad/humanid/feret/feret_master.html
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Chen, Ck., Yang, Jy. (2006). Discriminant Transform Based on Scatter Difference Criterion in Hidden Space. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_15
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DOI: https://doi.org/10.1007/11821045_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37597-5
Online ISBN: 978-3-540-37598-2
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