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Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis


Technical Report


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): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: MPI f. biologische Kybernetik, Tuebingen

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  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},
  month_numeric = {6}