Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis
2003
Technical Report
ei
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. |
Number (issue): | 109 |
Year: | 2003 |
Month: | June |
Day: | 0 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Technical Report (techreport) |
Institution: | MPI f. biologische Kybernetik, Tuebingen |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @techreport{2302, title = {Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis}, author = {Kim, KI. and Franz, M. and Sch{\"o}lkopf, B.}, number = {109}, organization = {Max-Planck-Gesellschaft}, institution = {MPI f. biologische Kybernetik, Tuebingen}, school = {Biologische Kybernetik}, month = jun, year = {2003}, doi = {}, month_numeric = {6} } |