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<p><b>Abstract</b>—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.</p>
Fisher's discriminant, nonlinear feature extraction, support vector machine, kernel functions, Rayleigh coefficient, oriented PCA.
Jason Weston, Alex Smola, Sebastian Mika, Klaus-Robert Müller, Bernhard Schölkopf, Gunnar Rätsch, "Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 623-633, May 2003, doi:10.1109/TPAMI.2003.1195996
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