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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.

Author(s): Harmeling, S. and Meinecke, F. and Müller, K-R.
Journal: Signal Processing
Volume: 84
Number (issue): 2
Pages: 255-266
Year: 2004
Month: February
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.sigpro.2003.10.009
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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  title = {Injecting noise for analysing the stability of ICA components},
  author = {Harmeling, S. and Meinecke, F. and M{\"u}ller, K-R.},
  journal = {Signal Processing},
  volume = {84},
  number = {2},
  pages = {255-266},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2004},
  month_numeric = {2}