@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}
}