Call for NIPS 2008 Kernel Learning Workshop Submissions
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S. Mika, G. Rätsch, B. Sch\"olkopf, A. Smola, J. Weston, and K.-R. Müller (1999)
Invariant Feature Extraction and Classification in Kernel Spaces
In: Advances in Neural Information Processing Systems 12, Cambridge, MA, MIT Press.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinear variant of the Rayleigh coefficient, we propose non-linear generalizations of Fisher's discriminant and oriented PCA using Support Vector kernel functions. Extensive simulations show the utility of our approach.