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B. Sch\"olkopf, J. Platt, and A.J. Smola (2000)

Kernel method for percentile feature extraction

Microsoft Research, TR MSR(2000-22), Redmond, WA.

A method is proposed which computes a direction in a dataset such that a specified fraction of a particular class of all examples is separated from the overall mean by a maximal margin. The projector onto that direction can be used for class-specific feature extraction. The algorithm is carried out in a feature space associated with a support vector kernel function, hence it can be used to construct a large class of nonlinear feature extractors. In the particular case where there exists only one class, the method can be thought of as a robust form of principal component analysis, where instead of variance we maximize percentile thresholds. Finally, we generalize it to also include the possibility of specifying negative examples.

by admin last modified 2007-01-31 11:08

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