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Multinomial sampling of latent variables for hierarchical change-point detection

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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.authorRomero Medrano, Lorena
dc.contributor.authorMoreno Múñoz, Pablo
dc.contributor.authorArtés Rodríguez, Antonio
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2023-09-25T15:19:06Z
dc.date.available2023-09-25T15:19:06Z
dc.date.issued2021-10-08
dc.description.abstractBayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.en
dc.description.sponsorshipThis work was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grants TEC2017-86921-C2-2-R (CAIMAN) and RTI2018-099655-B-I00 (CLARA), and by the Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM). The work of PMM has been supported by FPI grant BES-2016-077626 and ERC funding under the EU’s Horizon 2020 research and innovation programme (grant agreement nº 757360). LRM has been supported by grant IND2018/TIC-9649 from the Comunidad de Madrid.en
dc.identifier.bibliographicCitationRomero-Medrano, L., Moreno-Muñoz, P., & Artés-Rodríguez, A. (2021). Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection. Journal of Signal Processing Systems,94 (2), pp. 215-227.es
dc.identifier.doihttps://doi.org/10.1007/s11265-021-01705-8
dc.identifier.issn1939-8018
dc.identifier.publicationfirstpage215es
dc.identifier.publicationlastpage227es
dc.identifier.publicationtitleJournal of Signal Processing Systems for Signal Image and Video Technologyen
dc.identifier.publicationvolume94es
dc.identifier.urihttps://hdl.handle.net/10016/38442
dc.identifier.uxxiAR/0000029241
dc.language.isoenges
dc.publisherSpringeres
dc.relation.projectIDGobierno de España. BES-2016-077626es
dc.relation.projectIDGobierno de España. TEC2017-86921-C2-2-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.projectIDinfo:eu-repo/grantAgreement/EC/H2020/757360es
dc.relation.projectIDAT-2021
dc.rights© The author(s) 2021en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherBayesian inferenceen
dc.subject.otherChange-point detection (CPD)en
dc.subject.otherLatent variable modelsen
dc.subject.otherMultinomial likelihoodsen
dc.titleMultinomial sampling of latent variables for hierarchical change-point detectionen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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