Inlier-based ICA with an application to superimposed images
- This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) 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). (c) 2005 Wiley Periodicals, Inc
| Author details: | Frank C. Meinecke, Stefan Harmeling, Klaus-Robert Müller |
|---|---|
| ISSN: | 0899-9457 |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2005 |
| Publication year: | 2005 |
| Release date: | 2017/03/24 |
| Source: | International Journal of Imaging Systems and Technology. - ISSN 0899-9457. - 15 (2005), 1, S. 48 - 55 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
| Peer review: | Referiert |
| Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |
