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Pattern Analysis and Machine Intelligence, IEEE Transactions on In Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 25, No. 5. (2003), pp. 623-628, doi:10.1109/tpami.2003.1195996 Key: citeulike:4732503
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We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher's discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.
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