Complex valued robust multidimensional SOBI

Niko Lietzén*, Klaus Nordhausen, Pauliina Ilmonen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Complex valued random variables and time series are common in various applications, for example in wireless communications, radar applications and magnetic resonance imaging. These applications often involve the famous blind source separation problem. However, the observations rarely fully follow specific models and robust methods that allow deviations from the model assumptions and endure outliers are required. We propose a new algorithm, robust multidimensional eSAMSOBI, for complex valued blind source separation. The algorithm takes into account possible multidimensional spatial or temporal dependencies, whereas traditional SOBI-like procedures only consider dependencies in a single direction. In applications like functional magnetic resonance imaging, the dependencies are indeed not only one-dimensional. We provide a simulation study with complex valued data to illustrate the better performance of the methods that utilize multidimensional autocovariance in the presence of two-dimensional dependency. Moreover, we also examine the performance of the multidimensional eSAM-SOBI in the presence of outliers.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
Pages131-140
Number of pages10
Volume10169 LNCS
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Latent Variable Analysis and Signal Separation - Grenoble, France
Duration: 21 Feb 201723 Feb 2017
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10169 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceInternational Conference on Latent Variable Analysis and Signal Separation
Abbreviated titleLVA/ICA
Country/TerritoryFrance
CityGrenoble
Period21/02/201723/02/2017

Keywords

  • Complex valued BSS
  • Multidimensional autocovariance
  • SOBI
  • Time series

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