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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Magnusson, Måns
dc.contributor.author Jonsson, Leif
dc.contributor.author Villani, Mattias
dc.date.accessioned 2019-07-30T07:15:48Z
dc.date.available 2019-07-30T07:15:48Z
dc.date.issued 2020-03-01
dc.identifier.citation Magnusson , M , Jonsson , L & Villani , M 2020 , ' DOLDA : a regularized supervised topic model for high-dimensional multi-class regression ' , Computational Statistics , vol. 35 , no. 1 , pp. 175-201 . https://doi.org/10.1007/s00180-019-00891-1 en
dc.identifier.issn 0943-4062
dc.identifier.issn 1613-9658
dc.identifier.other PURE UUID: 3eb16b6c-aa7b-4183-b09d-cc5016c54f65
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/3eb16b6c-aa7b-4183-b09d-cc5016c54f65
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85067414496&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/35124979/Magnusson2019_Article_DOLDAARegularizedSupervisedTop.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/39416
dc.description.abstract Generating user interpretable multi-class predictions in data-rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle many classes as well as many covariates. To handle many classes we use the recently proposed Diagonal Orthant probit model (Johndrow et al., in: Proceedings of the sixteenth international conference on artificial intelligence and statistics, 2013) together with an efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al. in Biometrika 97:465–480, 2010). We propose a computationally efficient parallel Gibbs sampler for the new model. An important advantage of DOLDA is that learned topics are directly connected to individual classes without the need for a reference class. We evaluate the model’s predictive accuracy and scalability, and demonstrate DOLDA’s advantage in interpreting the generated predictions. en
dc.format.extent 27
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Springer Verlag
dc.relation.ispartofseries Computational Statistics en
dc.rights openAccess en
dc.title DOLDA en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Ericsson AB
dc.contributor.department Linköping University
dc.subject.keyword Diagonal Orthant probit model
dc.subject.keyword Horseshoe prior
dc.subject.keyword Interpretable models
dc.subject.keyword Latent Dirichlet Allocation
dc.subject.keyword Text classification
dc.identifier.urn URN:NBN:fi:aalto-201907304471
dc.identifier.doi 10.1007/s00180-019-00891-1
dc.type.version publishedVersion

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