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Handling incomplete heterogeneous data using VAEs

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorNazabal Renteria, Alfredo
dc.contributor.authorMartínez Olmos, Pablo
dc.contributor.authorGhahramani, Zoubin
dc.contributor.authorValera Martínez, María Isabel
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-05-25T09:50:11Z
dc.date.available2022-11-01T00:00:06Z
dc.date.issued2020-11
dc.description.abstractVariational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real valued, interval, categorical, ordinal and count data, and allows accurate estimation (and potentially imputation) of missing data. Furthermore, HI-VAE presents competitive predictive performance in supervised tasks, outperforming supervised models when trained on incomplete data.en
dc.description.sponsorshipThe authors wish to thank Christopher K. I. Williams, for fruitful discussions and helpful comments to the manuscript. Alfredo Nazabal would like to acknowledge the funding provided by the UK Government’s Defence & Security Programme in support of the Alan Turing Institute, EPSRC Grant EP/N510129/1. The work of Pablo M. Olmos is sup-ported by Spanish government MCI under grant RTI2018-099655-B-100, by Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, and Y2018/TCS-4705, by BBVA Foundation under the Deep-DARWiNproject, and by the European Union (FEDER and the European Research Council (ERC) through the European Unions Horizon 2020 research and innovation program under Grant 714161). Zoubin Ghahramani acknowledges support from the Alan Turing Institute (EPSRC Grant EP/N510129/1) and EPSRC Grant EP/N014162/1, and donations from Google and Microsoft Research. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.en
dc.format.extent11
dc.identifier.bibliographicCitationNazábal, A., Olmos, P. M., Ghahramani, Z. & Valera, I. (2020). Handling incomplete heterogeneous data using VAEs. Pattern Recognition, vol. 107, 107501.en
dc.identifier.doihttps://doi.org/10.1016/j.patcog.2020.107501
dc.identifier.issn0031-3203
dc.identifier.publicationfirstpage107501
dc.identifier.publicationtitlePattern Recognitionen
dc.identifier.publicationvolume107
dc.identifier.urihttps://hdl.handle.net/10016/32743
dc.identifier.uxxiAR/0000027141
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/714161
dc.relation.projectIDComunidad de Madrid. IND2017/TIC-7618es
dc.relation.projectIDComunidad de Madrid. IND2018/TIC-9649es
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705es
dc.relation.projectIDGobierno de España. RTI2018-099655-B-100es
dc.rights© 2020 Elsevier Ltd.
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherGenerative modelsen
dc.subject.otherVariational autoencodersen
dc.subject.otherIncomplete heterogenous dataen
dc.titleHandling incomplete heterogeneous data using VAEsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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