Wasserstein Stationary Subspace Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kaltenstadler, Stephan | - |
dc.contributor.author | Nakajima, Shinichi | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Samek, Wojciech | - |
dc.date.accessioned | 2021-09-02T02:32:03Z | - |
dc.date.available | 2021-09-02T02:32:03Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.issn | 1932-4553 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/71379 | - |
dc.description.abstract | Learning under nonstationarity can be achieved by decomposing the data into a subspace that is stationary and a nonstationary one [stationary subspace analysis (SSA)]. While SSA has been used in various applications, its robustness and computational efficiency have limits due to the difficulty in optimizing the Kullback-Leibler divergence based objective. In this paper, we contribute by extending SSA twofold: we propose SSA with 1) higher numerical efficiency by defining analytical SSA variants and 2) higher robustness by utilizing the Wasserstein-2 distance (Wasserstein SSA). We show the usefulness of our novel algorithms for toy data demonstrating their mathematical properties and for real-world data 1) allowing better segmentation of time series and 2) brain-computer interfacing, where theWasserstein-based measure of nonstationarity is used for spatial filter regularization and gives rise to higher decoding performance. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | OPTIMIZING SPATIAL FILTERS | - |
dc.subject | ALPHA-BETA | - |
dc.subject | DIVERGENCES | - |
dc.subject | METRICS | - |
dc.title | Wasserstein Stationary Subspace Analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1109/JSTSP.2018.2873987 | - |
dc.identifier.scopusid | 2-s2.0-85054534738 | - |
dc.identifier.wosid | 000454221700008 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, v.12, no.6, pp.1213 - 1223 | - |
dc.relation.isPartOf | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING | - |
dc.citation.title | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING | - |
dc.citation.volume | 12 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1213 | - |
dc.citation.endPage | 1223 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | OPTIMIZING SPATIAL FILTERS | - |
dc.subject.keywordPlus | ALPHA-BETA | - |
dc.subject.keywordPlus | DIVERGENCES | - |
dc.subject.keywordPlus | METRICS | - |
dc.subject.keywordAuthor | Subspace learning | - |
dc.subject.keywordAuthor | stationary subspace analysis | - |
dc.subject.keywordAuthor | divergence methods | - |
dc.subject.keywordAuthor | optimal transport | - |
dc.subject.keywordAuthor | covariance metrics | - |
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