A4 Article in conference proceedings
An Affine Equivariant Robust Second-Order BSS Method




List of Authors: Ilmonen P, Nordhausen K, Oja H, Theis FJ
Place: Berlin
Publication year: 2015
Journal: Lecture Notes in Computer Science
Book title *: Latent Variable Analysis and Signal Separation
Journal name in source: LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION, LVA/ICA 2015
Title of series: Lecture Notes in Computer Science
Number in series: 9237
Volume number: 9237
ISBN: 978-3-319-22481-7
ISSN: 0302-9743

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.



Last updated on 2019-29-01 at 18:43