Robust Prewhitening for ICA by Minimizing β-Divergence and Its Application to FastICA

Authors:
Md Nurul Mollah;Shinto Eguchi;Mihoko Minami
Affiliations:
Department of Statistical Science, The Graduate University for Advanced Studies, Tokyo, Japan 106-8569;The Institute of Statistical Mathematics, The Graduate University for Advanced Studies, Tokyo, Japan 106-8569;The Institute of Statistical Mathematics, The Graduate University for Advanced Studies, Tokyo, Japan 106-8569
Venue:
Neural Processing Letters
Year:
2007

Citing 7
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A fast fixed-point algorithm for independent component analysis

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Second Order Nonstationary Source Separation

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Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

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Exploring Latent Structure of Mixture ICA Models by the Minimum β-Divergence Method

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Fast and robust fixed-point algorithms for independent component analysis

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Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks

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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.