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Robust ICA for Super-Gaussian Sources

2004

Conference Paper

ei


Most ICA algorithms are sensitive to outliers. Instead of robustifying existing algorithms by outlier rejection techniques, we show how a simple outlier index can be used directly to solve the ICA problem for super-Gaussian source signals. This ICA method is outlier-robust by construction and can be used for standard ICA as well as for over-complete ICA (i.e. more source signals than observed signals (mixtures)).

Author(s): Meinecke, F. and Harmeling, S. and Müller, K-R.
Book Title: ICA 2004
Journal: Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004)
Pages: 217-224
Year: 2004
Month: October
Day: 0
Editors: Puntonet, C. G., A. Prieto
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/b100528
Event Name: Fifth International Conference on Independent Component Analysis and Blind Signal Separation
Event Place: Granada, Spain

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@inproceedings{6352,
  title = {Robust ICA for Super-Gaussian Sources},
  author = {Meinecke, F. and Harmeling, S. and M{\"u}ller, K-R.},
  journal = {Independent Component Analysis and Blind Signal Separation: Fifth International Conference (ICA 2004)},
  booktitle = {ICA 2004},
  pages = {217-224},
  editors = {Puntonet, C. G., A. Prieto},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {2004},
  month_numeric = {10}
}