Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

Citations Over TimeTop 10% of 2003 papers

Abstract

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.

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