Publication:
Multi-source change-point detection over local observation models

Simple item page
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.authorArtés Rodríguez, Antonio
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-05-29T10:56:28Z
dc.date.available2023-05-29T10:56:28Z
dc.date.issued2023-02
dc.description.abstractIn this work, we address the problem of change-point detection (CPD) on high-dimensional, multi-source, and heterogeneous sequential data with missing values. We present a new CPD methodology based on local latent variable models and adaptive factorizations that enhances the fusion of multi-source observations with different statistical data-type and face the problem of high dimensionality. Our motivation comes from behavioral change detection in healthcare measured by smartphone monitored data and Electronic Health Records. Due to the high dimension of the observations and the differences in the relevance of each source information, other works fail in obtaining reliable estimates of the change-points location. This leads to methods that are not sensitive enough when dealing with interspersed changes of different intensity within the same sequence or partial missing components. Through the definition of local observation models (LOMs), we transfer the local CP information to homogeneous latent spaces and propose several factorizations that weight the contribution of each source to the global CPD. With the presented methods we demonstrate a reduction in both the detection delay and the number of not-detected CPs, together with robustness against the presence of missing values on a synthetic dataset. We illustrate its application on real-world data from a smartphone-based monitored study and add explainability on the degree of each source contributing to the detection.en
dc.description.sponsorshipThis work has been partly supported by Spanish government (AEI/MCI) under grants RTI2018-099655-B-100, PID2021-123182OB-I00, PID2021-125159NB-I00, and TED2021-131823B-I00, by Comunidad de Madrid under grant IND2018/TIC-9649, IND2022/TIC- 23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM.en
dc.format.extent11
dc.identifier.bibliographicCitationRomero-Medrano, L., & Artés-Rodríguez, A. (2023). Multi-Source Change-Point Detection over Local Observation Models. Pattern Recognition, 134, 109116.en
dc.identifier.doihttps://doi.org/10.1016/j.patcog.2022.109116
dc.identifier.issn0031-3203
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue109116
dc.identifier.publicationlastpage11
dc.identifier.publicationtitlePattern Recognitionen
dc.identifier.publicationvolume134
dc.identifier.urihttps://hdl.handle.net/10016/37380
dc.identifier.uxxiAR/0000033066
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/714161
dc.relation.projectIDComunidad de Madrid. IND2018/TIC-9649es
dc.relation.projectIDGobierno de España. PID2021-125159NB-I00es
dc.relation.projectIDGobierno de España. PID2021-123182OB-I00es
dc.relation.projectIDComunidad de Madrid. IND2022/TIC-23550es
dc.relation.projectIDGobierno de España. RTI2018-099655-B-100es
dc.relation.projectIDGobierno de España. TED2021-131823B-I00es
dc.relation.projectIDAT-2022
dc.rights© 2022 The Author(s).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.otherChange-point detectionen
dc.subject.otherHeterogeneous dataen
dc.subject.otherLatent variable modelsen
dc.subject.otherMulti-source dataen
dc.titleMulti-source change-point detection over local observation modelsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Multi_PR_2023.pdf
Size:
1.88 MB
Format:
Adobe Portable Document Format
Download
Collections
DTSC - GTSA - Artículos de Revistas
OpenAIRE: Open Access Infrastructure for Research in Europe