Abstract
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 overcomplete ICA (i.e. more source signals than observed signals (mixtures)).
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Editors | Carlos G. Puntonet, Alberto Prieto |
| Publisher | Springer Verlag |
| Pages | 217-224 |
| Number of pages | 8 |
| ISBN (Electronic) | 3540230564, 9783540230564 |
| DOIs | |
| Publication status | Published - 2004 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3195 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
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Cite this
Meinecke, F. C., Harmeling, S., & Müller, K. R. (2004). Robust ICA for super-Gaussian sources. In C. G. Puntonet, & A. Prieto (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 217-224). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3195). Springer Verlag. https://doi.org/10.1007/978-3-540-30110-3_28