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Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, Sebastian; Raetsch, Gunnar; Weston, Jason; Schoelkopf, Bernhard; Smola, Alexander; Mueller, Klaus-Robert

Description

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 Raylelgh 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.

CollectionsANU Research Publications
Date published: 2003
Type: Journal article
URI: http://hdl.handle.net/1885/86087
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI: 10.1109/TPAMI.2003.1195996

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