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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Leppäaho, Eemeli
dc.contributor.author Ammad-ud-din, Muhammad
dc.contributor.author Kaski, Samuel
dc.date.accessioned 2017-08-03T12:09:19Z
dc.date.available 2017-08-03T12:09:19Z
dc.date.issued 2017
dc.identifier.citation Leppäaho , E , Ammad-ud-din , M & Kaski , S 2017 , ' GFA : Exploratory Analysis of Multiple Data Sources with Group Factor Analysis ' JOURNAL OF MACHINE LEARNING RESEARCH , vol 18 , 39 , pp. 1-5 . en
dc.identifier.issn 1532-4435
dc.identifier.issn 1533-7928
dc.identifier.other PURE UUID: 7cdd1e87-9dbd-4243-85f8-28defd293c3c
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/gfa(7cdd1e87-9dbd-4243-85f8-28defd293c3c).html
dc.identifier.other PURE LINK: http://jmlr.org/papers/v18/16-509.html
dc.identifier.other PURE LINK: http://www.jmlr.org/papers/volume18/16-509/16-509.pdf
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/14254602/16_509.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/27390
dc.description.abstract The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data. en
dc.format.extent 5
dc.format.extent 1-5
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries JOURNAL OF MACHINE LEARNING RESEARCH en
dc.relation.ispartofseries Volume 18 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title GFA en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.subject.keyword Bayesian latent variable modelling
dc.subject.keyword biclustering
dc.subject.keyword data integration
dc.subject.keyword factor analysis
dc.subject.keyword multi-view learning
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201708036358
dc.type.version publishedVersion


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