Abstract
This paper aims to establish a novel framework for high-performance Mercer kernel construction. Based on a given kernel matrix incorporated the class label information, a nonlinear mapping is firstly generated and well-defined on the training samples. The partial data-defined mapping can be extended and well-defined on the entire pattern space by means of interpolatory technology. The analytic expression of the nonlinear mapping is then obtained. It theoretically shows that the function K(x,y), created by the inner product of the nonlinear mapping, is a supervised Mercer kernel function. Our supervised kernel is successfully applied to unsupervised principal component analysis (PCA) method for face recognition. Two face databases, namely ORL and FERET databases, are selected for evaluations. Compared with KPCA with RBF kernel (RBF-PCA) method, experimental results demonstrate that KPCA with our supervised kernel (SK-PCA) has superior performance.
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References
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neurosicence 3, 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Schökopf, B., Smola, A., Müller, K.R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)
Kim, K., Jung, K., Kim, H.J.: Face Recognition Using Kernel Principal Component Analysis. IEEE Signal Processing Letters 9, 40–42 (2002)
Yang, J., Jin, Z., Yang, J.Y., Zhang, D., Frangi, A.F.: Essence of Kernel Fisher Discriminant: KPCA plus LDA. Pattern Recognition 37, 2097–2100 (2004)
Eftekhari, A., Forouzanfar, M., Abrishami Moghaddam, H., Alirezaie, J.: Block-Wise 2D Kernel PCA/LDA for Face Recognition. Information Processing Letters 110, 761–766 (2010)
Ebied, R.M.: Feature Extraction Using PCA and Kernel-PCA for Face Recognition. In: 8th International Conference on Informatics and Systems, pp. 72–77. IEEE Press, New York (2012)
Chen, W.S., Yuen, P.C.: Interpolatory Mercer Kernel Construction for Kernel Direct LDA on Face Recognition. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 857–860. IEEE Press, New York (2009)
Chu, W.S., Chen, J.C., James, L.J.J.: Kernel Discriminant Transformation for Image Set-Based Face Recognition. Pattern Recognition 44, 1567–1580 (2011)
Chan, C.H., Tahir, M.A., Kittler, J., Pietikäinen, M.: Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1164–1177 (2013)
Süli, E., Mayers, D.F.: An Introduction to Numerical Analysis. Cambridge University Press, Cambridge (2003)
Schölkopf, B., Smola, A.J.: Learning with Kernels-Support Vector Machine, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)
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Zhao, Y., Chen, WS., Pan, B., Chen, B. (2014). Supervised Kernel Construction for Unsupervised PCA on Face Recognition. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_37
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DOI: https://doi.org/10.1007/978-3-662-45643-9_37
Publisher Name: Springer, Berlin, Heidelberg
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