Abstract
Fisher discriminant based Foley-Sammon Transform (FST) has great influence in the area of pattern recognition. On the basis of FST, the Generalized Foley-Sammon Transform (GFST) is presented. The main difference between the GFST and the FST is that the transformed sample set by GFST has the best discriminant ability in global sense while FST has this property only in part sense. Linear discriminants are not always optimal, so a new nonlinear feature extraction method GFST with Kernels (KGFST) based on kernel trick is proposed in this paper. Linear feature extraction in feature space corresponds to non-linear feature extraction in input space. Then, KGFST is proved to correspond to a generalized eigenvalue problem. Lastly, our method is applied to digits and images recognition problems, and the experimental results show that present method is superior to the existing methods in term of space distribution and correct classification rate.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, Z., Li, L. (2005). Generalized Foley-Sammon Transform with Kernels. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_131
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DOI: https://doi.org/10.1007/11427391_131
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
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