Higher order stationary subspace analysis

dc.contributor.authorPanknin, Danny
dc.contributor.authorvon Bünau, Paul
dc.contributor.authorKawanabe, Motoaki
dc.contributor.authorMeinecke, Frank C.
dc.contributor.authorMüller, Klaus-Robert
dc.date.accessioned2022-03-30T12:01:27Z
dc.date.available2022-03-30T12:01:27Z
dc.date.issued2016
dc.date.updated2022-03-18T14:42:23Z
dc.description.abstractNon-stationarity in data is an ubiquitous problem in signal processing. The recent stationary subspace analysis procedure (SSA) has enabled to decompose such data into a stationary subspace and a non-stationary part respectively. Algorithmically only weak non- stationarities could be tackled by SSA. The present paper takes the conceptual step generalizing from the use of first and second moments as in SSA to higher order moments, thus defining the proposed higher order stationary subspace analysis procedure (HOSSA). The paper derives the novel procedure and shows simulations. An obvious trade-off between the necessity of estimating higher moments and the accuracy and robustness with which they can be estimated is observed. In an ideal setting of plenty of data where higher moment information is dominating our novel approach can win against standard SSA. However, with limited data, even though higher moments actually dominate the underlying data, still SSA may arrive on par.en
dc.description.sponsorshipBMBF, 01IB15001B, Verbundprojekt: ALICE II - Autonomes Lernen in komplexen Umgebungen 2 (Autonomous Learning in Complex Environments 2)en
dc.description.sponsorshipBMBF, 01GQ1115, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungenen
dc.description.sponsorshipDFG, 200318152, Theoretische Konzepte für co-adaptive Mensch-Maschine-Interaktion mit Anwendungen auf BCIen
dc.identifier.eissn1742-6596
dc.identifier.issn1742-6588
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16598
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-15375
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en
dc.subject.ddc530 Physikde
dc.subject.otherStationary subspace analysisen
dc.subject.otherblind source separationen
dc.subject.othernon-stationary dataen
dc.subject.othermultivariate time series analysisen
dc.subject.otherdimensionality reductionen
dc.titleHigher order stationary subspace analysisen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber012021en
dcterms.bibliographicCitation.doi10.1088/1742-6596/699/1/012021en
dcterms.bibliographicCitation.issue1en
dcterms.bibliographicCitation.journaltitleJournal of Physics: Conference Seriesen
dcterms.bibliographicCitation.originalpublishernameIOPen
dcterms.bibliographicCitation.originalpublisherplaceBristolen
dcterms.bibliographicCitation.volume699en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Softwaretechnik und Theoretische Informatik>FG Maschinelles Lernende
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Maschinelles Lernende
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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