Employing a uni ed framework in terms of a nonlinear variant of the Rayleigh coecient, 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.
It is common practice to preprocess data by extracting linear or nonlinear features. Invariant feature extraction and classification in kernel spaces. MIT Press, 2000. Invariant feature extraction and classification in kernel spaces. MIT Press, 2000.