Ilmonen P, Nordhausen K, Oja H, Theis F (2015)
Publication Type: Conference contribution
Publication year: 2015
Publisher: Springer Verlag
Book Volume: 9237
Pages Range: 328-335
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Liberec, CZE
ISBN: 9783319224817
DOI: 10.1007/978-3-319-22482-4_38
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 eSAMSOBI 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.
APA:
Ilmonen, P., Nordhausen, K., Oja, H., & Theis, F. (2015). An affine equivariant robust second-order BSS method. In Zbynĕk Koldovský, Emmanuel Vincent, Arie Yeredor, Petr Tichavský (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 328-335). Liberec, CZE: Springer Verlag.
MLA:
Ilmonen, Pauliina, et al. "An affine equivariant robust second-order BSS method." Proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2015, Liberec, CZE Ed. Zbynĕk Koldovský, Emmanuel Vincent, Arie Yeredor, Petr Tichavský, Springer Verlag, 2015. 328-335.
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