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Uncorrelated discriminant vectors using a kernel method. (English) Zbl 1101.68818
Summary: Uncorrelated discriminant vectors using a kernel method are proposed in this paper. In some sense, kernel uncorrelated discriminant vectors extend Jin’s method and then several related theorems are stated. Most importantly, the proposed method can deal with nonlinear problems. Finally, experimental results on handwritten numeral characters show that the proposed method is effective and feasible.

MSC:
68T10 Pattern recognition, speech recognition
Software:
UCI-ml
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References:
[1] Jin, Z.; Yang, J.Y.; Hu, Z.S.; Luo, Z., Face recognition based on the uncorrelated discriminant transformation, Pattern recognition, 34, 7, 1405-1416, (2001) · Zbl 0978.68118
[2] Scholkopf, B.; Smola, A.; Muller, K.R., Nonlinear component analysis as a kernel eigenvalue problem, Neural comput., 10, 5, 1299-1319, (1998)
[3] Baudat, G.; Anouar, F., Generalized discriminant analysis using a kernel approach, Neural comput., 12, 10, 2385-2404, (2000)
[4] Yang, J.; Jin, Z.; Yang, J.; Zhang, D., The essence of kernel Fisher discriminantkpca plus LDA, Pattern recognition, 37, 10, 2097-2100, (2004)
[5] C. Blake, E. Keogh, C.J. Merz, UCI Repository of machine learning databases [http://www.ics.uci.edu/\(\sim\)mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
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