Complex valued robust multidimensional SOBI

Niko Lietzén*, Klaus Nordhausen, Pauliina Ilmonen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoLatent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
KustantajaSpringer
Sivut131-140
Sivumäärä10
Vuosikerta10169 LNCS
ISBN (painettu)9783319535463
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Latent Variable Analysis and Signal Separation - Grenoble, Ranska
Kesto: 21 helmik. 201723 helmik. 2017
Konferenssinumero: 13

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta10169 LNCS
ISSN (painettu)03029743
ISSN (elektroninen)16113349

Conference

ConferenceInternational Conference on Latent Variable Analysis and Signal Separation
LyhennettäLVA/ICA
Maa/AlueRanska
KaupunkiGrenoble
Ajanjakso21/02/201723/02/2017

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