Kernel method for percentile feature extraction
2000
Technical Report
ei
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
Author(s): | Schölkopf, B. and Platt, JC. and Smola, AJ. |
Number (issue): | MSR-TR-2000-22 |
Year: | 2000 |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Technical Report (techreport) |
Institution: | Microsoft Research |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @techreport{1836, title = {Kernel method for percentile feature extraction}, author = {Sch{\"o}lkopf, B. and Platt, JC. and Smola, AJ.}, number = {MSR-TR-2000-22}, organization = {Max-Planck-Gesellschaft}, institution = {Microsoft Research}, school = {Biologische Kybernetik}, year = {2000}, doi = {} } |