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): | Empirische Inferenz |
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}, doi = {10.1109/TPAMI.2003.1195996}, month_numeric = {5} } |