@inproceedings{,
author={Kocsor, Andr{\'a}s and Csirik, J{\'a}nos},
title={Fast Independent Component Analysis in Kernel Feature Spaces},
abstract={It is common practice to apply linear or nonlinear feature extraction methods
before classification. Usually linear methods are faster and simpler than
nonlinear ones but an idea successfully employed in the nonlinearization
of Support Vector Machines permits a simple and effective extension of
several statistical methods to their nonlinear counterparts. In this paper
we follow this general nonlinearization approach in the context of Independent
Component Analysis, which is a general purpose statistical method for blind
source separation and feature extraction. In addition, nonlinearized formulae
are furnished along with an illustration of the usefulness of the proposed
method as an unsupervised feature extractor for the classification of Hungarian
phonemes. },
booktitle={SOFSEM 2001: Theory and Practice of Informatics: 28th Conference on Current
Trends in Theory and Practice of Informatics, LNAI vol. 2234},
year={2001},
month={November},
publisher={Springer-Verlag GmbH },
address={Piestany, Slovak Republic},
editor={L. Pacholski and P. Ruzicka},
pages={271-281}
}