Abstract
The interest in robust methods for blind source separation has increased recently. In this paper we shortly review what has been suggested so far for robustifying ICA and second order blind source separation. Furthermore do we suggest a new algorithm, eSAM-SOBI, which is an affine equivariant improvement of (already robust) SAM-SOBI. In a simulation study we illustrate the benefits of using eSAM-SOBI when compared to SOBI and SAM-SOBI. For uncontaminated time series SOBI and eSAM-SOBI perform equally well. However, SOBI suffers a lot when the data is contaminated by outliers, whereas robust eSAM-SOBI does not. Due to the lack of affine equivariance of SAM-SOBI, eSAM-SOBI performs clearly better than it for both, contaminated and uncontaminated data.
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Acknowledgements
The work of Klaus Nordhausen and Hannu Oja was supported by the Academy of Finland (grant 268703). The authors thank the referees for their insightful comments which helped to improve the manuscript.
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Ilmonen, P., Nordhausen, K., Oja, H., Theis, F. (2015). An Affine Equivariant Robust Second-Order BSS Method. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_38
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DOI: https://doi.org/10.1007/978-3-319-22482-4_38
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