Empirical Inference
Technical Report
2003
Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis
PDF
Empirical Inference
A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method, a convergence proof, and preliminary applications in image hyperresolution are presented. In addition, we discuss the extension of the method to the online learning of kernel principal components.
| Author(s): | Kim, KI. and Franz, M. and Schölkopf, B. |
| Links: | |
| Number (issue): | 109 |
| Year: | 2003 |
| Month: | June |
| Day: | 0 |
| BibTeX Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | MPI f. biologische Kybernetik, Tuebingen |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@techreport{2302,
title = {Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis},
abstract = {A new method for performing a kernel principal component analysis is
proposed. By kernelizing the generalized Hebbian algorithm, one can
iteratively estimate the principal components in a reproducing
kernel Hilbert space with only linear order memory complexity. The
derivation of the method, a convergence proof, and preliminary
applications in image hyperresolution are presented. In addition,
we discuss the extension of the method to the online learning of
kernel principal components.},
number = {109},
organization = {Max-Planck-Gesellschaft},
institution = {MPI f. biologische Kybernetik, Tuebingen},
school = {Biologische Kybernetik},
month = jun,
year = {2003},
author = {Kim, KI. and Franz, M. and Sch{\"o}lkopf, B.},
month_numeric = {6}
}