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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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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