Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Kernel method for percentile feature extraction

There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

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
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