Higher order stationary subspace analysis
| dc.contributor.author | Panknin, Danny | |
| dc.contributor.author | von Bünau, Paul | |
| dc.contributor.author | Kawanabe, Motoaki | |
| dc.contributor.author | Meinecke, Frank C. | |
| dc.contributor.author | Müller, Klaus-Robert | |
| dc.date.accessioned | 2022-03-30T12:01:27Z | |
| dc.date.available | 2022-03-30T12:01:27Z | |
| dc.date.issued | 2016 | |
| dc.date.updated | 2022-03-18T14:42:23Z | |
| dc.description.abstract | Non-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.sponsorship | BMBF, 01IB15001B, Verbundprojekt: ALICE II - Autonomes Lernen in komplexen Umgebungen 2 (Autonomous Learning in Complex Environments 2) | en |
| dc.description.sponsorship | BMBF, 01GQ1115, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen | en |
| dc.description.sponsorship | DFG, 200318152, Theoretische Konzepte für co-adaptive Mensch-Maschine-Interaktion mit Anwendungen auf BCI | en |
| dc.identifier.eissn | 1742-6596 | |
| dc.identifier.issn | 1742-6588 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/16598 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-15375 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | en |
| dc.subject.ddc | 530 Physik | de |
| dc.subject.other | Stationary subspace analysis | en |
| dc.subject.other | blind source separation | en |
| dc.subject.other | non-stationary data | en |
| dc.subject.other | multivariate time series analysis | en |
| dc.subject.other | dimensionality reduction | en |
| dc.title | Higher order stationary subspace analysis | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 012021 | en |
| dcterms.bibliographicCitation.doi | 10.1088/1742-6596/699/1/012021 | en |
| dcterms.bibliographicCitation.issue | 1 | en |
| dcterms.bibliographicCitation.journaltitle | Journal of Physics: Conference Series | en |
| dcterms.bibliographicCitation.originalpublishername | IOP | en |
| dcterms.bibliographicCitation.originalpublisherplace | Bristol | en |
| dcterms.bibliographicCitation.volume | 699 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Maschinelles Lernen | de |
| tub.affiliation.institute | Inst. Softwaretechnik und Theoretische Informatik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |