Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces
S. Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander Johannes Smola, K. Robert Müller · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2003
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.