Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning

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dc.contributor.authorXu, Ruiyuen
dc.contributor.authorWu, Jianguoen
dc.contributor.authorYue, Xiaoweien
dc.contributor.authorLi, Yongxiangen
dc.date.accessioned2023-02-07T17:34:21Zen
dc.date.available2023-02-07T17:34:21Zen
dc.date.issued2022-03-30en
dc.date.updated2023-02-05T03:28:50Zen
dc.description.abstractHigh-dimensional streaming data are becoming increasingly ubiquitous in many fields. They often lie in multiple low-dimensional subspaces, and the manifold structures may change abruptly on the time scale due to pattern shift or occurrence of anomalies. However, the problem of detecting the structural changes in a real-time manner has not been well studied. To fill this gap, we propose a dynamic sparse subspace learning approach for online structural change-point detection of high-dimensional streaming data. A novel multiple structural change-point model is proposed and the asymptotic properties of the estimators are investigated. A tuning method based on Bayesian information criterion and change-point detection accuracy is proposed for penalty coefficients selection. An efficient Pruned Exact Linear Time based algorithm is proposed for online optimization and change-point detection. The effectiveness of the proposed method is demonstrated through several simulation studies and a real case study on gesture data for motion tracking. Supplementary materials for this article are available online.en
dc.description.versionAccepted versionen
dc.format.extent14 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/00401706.2022.2046171en
dc.identifier.eissn1537-2723en
dc.identifier.issn0040-1706en
dc.identifier.orcidYue, Xiaowei [0000-0001-6019-0940]en
dc.identifier.urihttp://hdl.handle.net/10919/113700en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000777095200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectHigh-dimensional time seriesen
dc.subjectManifold learningen
dc.subjectMultiple change-point modelen
dc.subjectSubspace clusteringen
dc.subjectGRAPHICAL MODELSen
dc.subjectALGORITHMen
dc.subjectSELECTIONen
dc.subjectNETWORKSen
dc.titleOnline Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learningen
dc.title.serialTechnometricsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2022-01-05en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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