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dc.contributor.authorAlcacer Sales, Aleix
dc.contributor.authorEpifanio, Irene
dc.contributor.authorGual-Arnau, Ximo
dc.date.accessioned2024-06-05T10:10:32Z
dc.date.available2024-06-05T10:10:32Z
dc.date.issued2024-05-13
dc.identifier.citationA. Alcacer, I. Epifanio and X. Gual-Arnau, "Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2024.3400730.ca_CA
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/10234/207749
dc.description.abstractWe introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to represent observations and features through instances of pure types, or biarchetypes, which are easily interpretable as they embody mixtures of observations and features. Furthermore, the observations and features are expressed as mixtures of the biarchetypes, which makes the structure of the data easier to understand. We propose an algorithm to solve biarchetype analysis. Although clustering is not the primary aim of this technique, biarchetype analysis is demonstrated to offer significant advantages over biclustering methods, particularly in terms of interpretability. This is attributed to biarchetypes being extreme instances, in contrast to the centroids produced by biclustering, which inherently enhances human comprehension. The application of biarchetype analysis across various machine learning challenges underscores its value, and both the source code and examples are readily accessible in R and Python at https://github.com/aleixalcacer/JA-BIAA .ca_CA
dc.description.sponsorShipFunding for open access charge: CRUE-Universitat Jaume I
dc.format.extent12 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherIEEE Computer Societyca_CA
dc.relation.isPartOfIEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.ca_CA
dc.rights© The Authorsca_CA
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/ca_CA
dc.subjectArchetype analysisca_CA
dc.subjectbiclusteringca_CA
dc.subjectprototypeca_CA
dc.subjectunsupervised learningca_CA
dc.subjectGene expressionca_CA
dc.subjectElbowca_CA
dc.subjectSportsca_CA
dc.subjectSociologyca_CA
dc.subjectProposalsca_CA
dc.subjectData analysisca_CA
dc.titleBiarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doi10.1109/TPAMI.2024.3400730
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://ieeexplore.ieee.org/abstract/document/10530052ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameMinisterio de Ciencia e Innovaciónca_CA
project.funder.nameUniversitat Jaume Ica_CA
oaire.awardNumberPID2022-141699NB-I00, PID2020-118763GA-I00, PID2020-115930GA-I00ca_CA
oaire.awardNumberUJI-B2020-22, TRANSUJI/2023/6ca_CA


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