Robust Prewhitening for ICA by Minimizing <Emphasis Type="Italic">β</Emphasis>-Divergence and Its Application to FastICA |
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Authors: | Md Nurul Haque Mollah Shinto Eguchi Mihoko Minami |
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Affiliation: | (1) Department of Statistical Science, The Graduate University for Advanced Studies, Minato-ku, Tokyo 106-8569, Japan;(2) The Institute of Statistical Mathematics, The Graduate University for Advanced Studies, Minato-ku, Tokyo 106-8569, Japan |
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Abstract: | 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. |
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Keywords: | independent component analysis β -prewhitening robustness adaptive selection one standard error |
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