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Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

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6 Author(s)
Mika, S. ; Fraunhofer FIRST, Berlin, Germany ; Ratsch, G. ; Weston, J. ; Scholkopf, B.
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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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:25 ,  Issue: 5 )