Empirical Inference
Article
2003
Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces
Empirical Inference
Empirical Inference
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.
| Author(s): | Mika, S. and Rätsch, G. and Weston, J. and Schölkopf, B. and Smola, AJ. and Müller, K-R. |
| Journal: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume: | 25 |
| Number (issue): | 5 |
| Pages: | 623-628 |
| Year: | 2003 |
| Month: | May |
| Day: | 0 |
| BibTeX Type: | Article (article) |
| DOI: | 10.1109/TPAMI.2003.1195996 |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@article{1844,
title = {Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
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.},
volume = {25},
number = {5},
pages = {623-628},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = may,
year = {2003},
author = {Mika, S. and R{\"a}tsch, G. and Weston, J. and Sch{\"o}lkopf, B. and Smola, AJ. and M{\"u}ller, K-R.},
doi = {10.1109/TPAMI.2003.1195996},
month_numeric = {5}
}