Robust Prewhitening for ICA by Minimizing β-Divergence and Its Application to FastICA
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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.
Keywordsindependent component analysis β-prewhitening robustness adaptive selection one standard error
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- 3.Cichocki A., Amari S. (2002) Adaptive Blind Signal and Image Processing. Wiley, New YorkGoogle Scholar
- 5.Cardoso, J.-F.: Source separation using high order moments, In: Proceedings of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP’89), pp. 2109–2112, Glasgow, UK, 1989. Neural Computation 11 (1999), 157–192.Google Scholar
- 8.Hyvärinen, A.: One-unit contrast functions for independent component analysis: A statistical analysis, In: Neural Networks for Signal Processing VII (Proceedings of IEEE Workshop on Neural Networks for Signal Processing). pp. 388–397, Amelia Island,Google Scholar
- 11.Hyvärinen A, Karhunen J., Oja E. (2001) Independent Component Analysis. Wiley, New YorkGoogle Scholar
- 13.Minami, M. and Eguchi, S.: Adaptive selection for minimum β-divergence method, Proceedings of ICA-2003 Conference, Nara, JapanGoogle Scholar