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dc.contributor.authorHorev, Inbal
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
dc.contributor.authorSugiyama, Masashi
dc.date.accessioned2017-01-27T13:11:33Z
dc.date.available2017-01-27T13:11:33Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16213
dc.language.isoenen
dc.subjectStationary subspace analysis
dc.subjectdimensionality reduction
dc.subjectRiemannian geometry
dc.subjectSPD manifold
dc.subjectGrassmann manifold
dc.subject.ddc516; 519en
dc.titleGeometry-aware stationary subspace analysis
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherUniverstité de Tokyo
dc.description.abstractenIn many real-world applications data exhibits non-stationarity, i.e., its distribution changes over time. One approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface (BCI) data analysis this is sometimes achieved using stationary subspace analysis (SSA). The classic SSA method finds a matrix that projects the data onto a stationary subspace by optimizing a cost function based on a matrix divergence. In this work we present an alternative method for SSA based on a symmetrized version of this matrix divergence. We show that this frames the problem in terms of distances between symmetric positive definite (SPD) matrices, suggesting a geometric interpretation of the problem. Stemming from this geometric viewpoint, we introduce and analyze a method which utilizes the geometry of the SPD matrix manifold and the invariance properties of its metrics. Most notably we show that these invariances alleviate the need to whiten the input matrices, a common step in many SSA methods which often introduces error. We demonstrate the usefulness of our technique in experiments on both synthetic and real-world data.
dc.identifier.citationpages430-444
dc.relation.ispartoftitleProceedings of The 8th Asian Conference on Machine Learning (ACML 2016)
dc.relation.ispartofeditorRobert J. Durrant, Kee-Eung Kim
dc.relation.ispartofpublnameJMLR: Workshop and Conference Proceedings
dc.relation.ispartofdate2016
dc.identifier.urlsitehttp://jmlr.org/proceedings/papers/v63/Horev84.html
dc.contributor.countryeditoruniversityotherJAPAN
dc.subject.ddclabelGéométrie; Probabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-01-27T13:12:35Z
hal.identifierhal-01447959*
hal.version1*
hal.update.actionupdateFiles*


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