Kernel method for percentile feature extraction

Schölkopf, B; Platt, JC; Smola, AJ

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Kernel method for percentile feature extraction

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https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2000-22.pdf
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Schölkopf, B., Platt, J., & Smola, A.(2000). Kernel method for percentile feature extraction (MSR-TR-2000-22). Redmond, WA, USA: Microsoft Research, Microsoft Corporation.

Cite as: https://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