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Sparse semi-supervised heterogeneous interbattery bayesian analysis

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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.authorSevilla Salcedo, Carlos
dc.contributor.authorGómez Verdejo, Vanessa
dc.contributor.authorMartínez Olmos, Pablo
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-12-09T08:44:57Z
dc.date.available2023-12-01T00:00:05Z
dc.date.issued2021-12
dc.description.abstractThe Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.en
dc.description.sponsorshipThe authors wish to thank Irene Santos, for fruitful discussions and help during the earlier stages of our work. The work of Pablo M. Olmos was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. C. Sevilla-Salcedo and V. Gómez-Verdejo's work has been partly funded by the Spanish MINECO grants TEC2017-83838-R and PID2020-115363RB-I00.en
dc.format.extent13
dc.identifier.bibliographicCitationSevilla, C., Gómez, V. & Olmos, P. M. (2021). Sparse semi-supervised heterogeneous interbattery bayesian analysis. Pattern Recognition, 120, 108141.en
dc.identifier.doihttps://doi.org/10.1016/j.patcog.2021.108141
dc.identifier.issn0031-3203
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue108141
dc.identifier.publicationlastpage13
dc.identifier.publicationtitlePattern Recognitionen
dc.identifier.publicationvolume120
dc.identifier.urihttps://hdl.handle.net/10016/33724
dc.identifier.uxxiAR/0000028558
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/714161
dc.relation.projectIDComunidad de Madrid. IND2017/TIC-7618es
dc.relation.projectIDGobierno de España. TEC2017-83838-Res
dc.relation.projectIDGobierno de España. TEC2017-92552-EXPes
dc.relation.projectIDComunidad de Madrid. IND2018/TIC-9649es
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705es
dc.relation.projectIDGobierno de España. RTI2018-099655-B-I00es
dc.relation.projectIDComunidad de Madrid. IND2020/TIC-17372es
dc.relation.projectIDGobierno de España. PID2020-115363RB-I00es
dc.rights© 2021 Elsevier Ltd. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherBayesian modelen
dc.subject.otherCanonical correlation analysisen
dc.subject.otherPrincipal component analysisen
dc.subject.otherFactor analysisen
dc.subject.otherFeature selectionen
dc.subject.otherSemi-superviseden
dc.subject.otherMulti-tasken
dc.titleSparse semi-supervised heterogeneous interbattery bayesian analysisen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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