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dc.contributor.authorRoald, Marie
dc.date.accessioned2023-02-21T08:13:38Z
dc.date.available2023-02-21T08:13:38Z
dc.date.created2023-02-07T06:57:34Z
dc.date.issued2023-01-04
dc.identifier.issn2352-7110
dc.identifier.urihttps://hdl.handle.net/11250/3052545
dc.description.abstractCoupled matrix factorization (CMF) models jointly decompose a collection of matrices with one shared mode. For interpretable decompositions, constraints are often needed, and variations of constrained CMF models have been used in various fields, including data mining, chemometrics and remote sensing. Although such models are broadly used, there is a lack of easy-to-use, documented, and open-source implementations for fitting CMFs with user-specified constraints on all modes. We address this need with MatCoupLy, a Python package that implements a state-of-the-art algorithm for CMF and PARAFAC2 that supports any proximable constraint on any mode. This paper outlines the functionality of MatCoupLy, including three examples demonstrating the flexibility and extendibility of the package.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesSoftwareX;Volume 21, February 2023, 101292
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleMatCoupLy: Learning coupled matrix factorizations with Pythonen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2022 The Author(s)en_US
dc.source.articlenumber101292en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1016/j.softx.2022.101292
dc.identifier.cristin2123512
dc.source.journalSoftwareXen_US
dc.source.volume21en_US
dc.source.issue21en_US
dc.source.pagenumber1-9en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal