Referenced Items are not available for this document.
A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve “source adaptivity” by directly estimating from the data the “true” score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function
Published in:
Neural Networks, IEEE Transactions on
(Volume:12
,
Issue:
3
)
Date of Publication: May 2001
- Page(s):
- 559 - 566
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 6948555
- Digital Object Identifier :
- 10.1109/72.925558
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 07 August 2002
- Issue Date :
- May 2001
- Sponsored by :
- IEEE Computational Intelligence Society
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
