Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 255-266 |
Number of pages | 12 |
Journal | Signal Processing |
Volume | 84 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2004 Feb |
Bibliographical note
Funding Information:The authors wish to thank the reviewers, furthermore also Gilles Blanchard, Benjamin Blankertz, Motoaki Kawanabe, and Andreas Ziehe for valuable discussions. This work was partly supported by the EU project (IST-1999-14190 – BLISS) and the support from the BMBF under contract FKZ 01IBB02A.
Keywords
- ICA
- Noise injection
- Stability
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering