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#### Kernel method for percentile feature extraction

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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