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dc.contributor.authorPan, Yan
dc.contributor.authorMatilainen, Markus
dc.contributor.authorTaskinen, Sara
dc.contributor.authorNordhausen, Klaus
dc.date.accessioned2021-11-12T06:57:50Z
dc.date.available2021-11-12T06:57:50Z
dc.date.issued2021
dc.identifier.citationPan, Y., Matilainen, M., Taskinen, S., & Nordhausen, K. (2021). A review of second‐order blind identification methods. <i>WIREs Computational Statistics</i>, <i>Early View</i>. <a href="https://doi.org/10.1002/wics.1550" target="_blank">https://doi.org/10.1002/wics.1550</a>
dc.identifier.otherCONVID_47860645
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/78633
dc.description.abstractSecond order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modelling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source signals from an observed signal mixture. The SOS model assumes that the observed time series (signals) is a linear mixture of latent time series (sources) with uncorrelated components. The methods make use of the second order statistics - hence the name “second order source separation”. In this review we discuss the classical SOS methods and their extensions to more complex settings. An example illustrates how SOS can be performed.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherJohn Wiley & Sons
dc.relation.ispartofseriesWIREs Computational Statistics
dc.rightsCC BY 4.0
dc.titleA review of second‐order blind identification methods
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202111125648
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1939-5108
dc.relation.volumeEarly View
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 The Authors. WIREs Computational Statistics published by Wiley Periodicals LLC
dc.rights.accesslevelopenAccessfi
dc.subject.ysoaikasarja-analyysi
dc.subject.ysolaskennallinen tiede
dc.subject.ysosignaalinkäsittely
dc.subject.ysoaikasarjat
dc.subject.ysomonimuuttujamenetelmät
dc.subject.ysotilastomenetelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p22747
jyx.subject.urihttp://www.yso.fi/onto/yso/p21978
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p12290
jyx.subject.urihttp://www.yso.fi/onto/yso/p2131
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1002/wics.1550
jyx.fundinginformationThe work of KN was supported by the Austrian Science Fund P31881-N32.


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