Injecting noise for analysing the stability of ICA components
- Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data. (C) 2003 Elsevier B.V. All rights reserved
| Author details: | Stefan Harmeling, Frank C. Meinecke, Klaus-Robert Müller |
|---|---|
| ISSN: | 0165-1684 |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2004 |
| Publication year: | 2004 |
| Release date: | 2017/03/24 |
| Source: | Signal Processing. - ISSN 0165-1684. - 84 (2004), 2, S. 255 - 266 |
| 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 |
