Robust prewhitening for ICA by minimizing β-divergence and its application to FastICA

Md Nurul Haque Mollah, Shinto Eguchi, Mihoko Minami

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Many estimation methods for independent component analysis (ICA) requires prewhitening of observed signals. This paper proposes a new method of prewhitening named β-prewhitening by minimizing the empirical β-divergence over the space of all the Gaussian distributions. The value of the tuning parameter β plays the key role in the performance of our current proposal. An attempt is made to propose an adaptive selection procedure for the tuning parameter β for this algorithm. At last, a measure of performance index is proposed for assessing prewhitening procedures. Simulation results show that β-prewhitening efficiently improves the performance over the standard prewhitening when outliers exist; it keeps equal performance otherwise. Performance of the proposed method is compared with the standard prewhitening by both FastICA and our proposed performance index.

Original languageEnglish
Pages (from-to)91-110
Number of pages20
JournalNeural Processing Letters
Issue number2
Publication statusPublished - 2007 Apr
Externally publishedYes


  • Adaptive selection
  • Independent component analysis
  • One standard error
  • Robustness
  • β-prewhitening

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence


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