@inproceedings{4295,
  title = {Fast Kernel ICA using an Approximate Newton Method},
  journal = {Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS  2007)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007},
  abstract = {Recent approaches to independent component analysis (ICA) have used
  kernel independence measures to obtain very good performance,
  particularly where classical methods experience difficulty (for
  instance, sources with near-zero kurtosis).  We present Fast Kernel ICA
  (FastKICA), a novel optimisation technique for one such kernel
  independence measure, the Hilbert-Schmidt independence criterion
  (HSIC).  Our search procedure uses an approximate Newton method on the
  special orthogonal group, where we estimate the Hessian locally about
  independence.  We employ incomplete Cholesky decomposition to
  efficiently compute the gradient and approximate Hessian. FastKICA results in more accurate solutions at a given cost
  compared with gradient descent, and is relatively insensitive to local minima
  when initialised far from independence.  These properties allow kernel approaches to be
  extended to problems with larger numbers of sources and observations.
  Our method is  competitive with other modern and classical ICA
  approaches in both speed and accuracy.},
  pages = {476-483},
  editors = {Meila, M. , X. Shen},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = mar,
  year = {2007},
  slug = {4295},
  author = {Shen, H. and Jegelka, S. and Gretton, A.},
  month_numeric = {3}
}