Constructing Descriptive and Discriminative Non-linear Features: Rayleigh 
Coefficients in Kernel Feature Spaces

Mika, S; Rätsch, G; Weston, J; Schölkopf, B; Smola, AJ; Müller, K-R

doi:10.1109/TPAMI.2003.1195996

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

MPS-Authors

/persons/resource/persons84153

Rätsch, G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84311

Weston, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84096

Müller, K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

No external resources are shared

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

Citation

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., & Müller, K.-R. (2003). Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 623-628. doi:10.1109/TPAMI.2003.1195996.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DC7F-8

Abstract

We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.