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  Kernel method for percentile feature extraction

Schölkopf, B., Platt, J., & Smola, A.(2000). Kernel method for percentile feature extraction (MSR-TR-2000-22). Redmond, WA, USA: Microsoft Research, Microsoft Corporation.

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 Creators:
Schölkopf, B1, Author           
Platt, JC, Author
Smola, AJ, Author           
Affiliations:
1External Organizations, ou_persistent22              

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 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

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 Dates: 2000-02
 Publication Status: Published in print
 Pages: -
 Publishing info: Redmond, WA, USA : Microsoft Research, Microsoft Corporation
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: MSR-TR-2000-22
BibTex Citekey: 1836
 Degree: -

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