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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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:25 ,  Issue: 5 )

Date of Publication:

May 2003

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