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

2003

Article

ei


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

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/TPAMI.2003.1195996
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},
  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.},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {25},
  number = {5},
  pages = {623-628},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = may,
  year = {2003},
  month_numeric = {5}
}