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Kernel method for percentile feature extraction

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B., Platt, J., & Smola, A.(2000). Kernel method for percentile feature extraction (MSR-TR-2000-22).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E5E4-A
Abstract
A method is proposed which computes a direction in a dataset such that a specied fraction of a particular class of all examples is separated from the overall mean by a maximal margin The pro jector onto that direction can be used for classspecic 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 fea ture 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 Fi nally we generalize it to also include the possibility of specifying negative examples