Mostrar el registro sencillo del ítem

dc.contributor.authorEpifanio, Irene
dc.contributor.authorGimeno, Vicent
dc.contributor.authorGual-Arnau, Ximo
dc.contributor.authorIbáñez Gual, Maria Victoria
dc.date.accessioned2023-09-28T09:32:49Z
dc.date.available2023-09-28T09:32:49Z
dc.date.issued2023
dc.identifier.citationEpifanio, I., Gimeno, V., Gual-Arnau, X. et al. Archetypal Curves in the Shape and Size Space: Discovering the Salient Features of Curved Big Data by Representative Extremes. La Matematica 2, 635–658 (2023). https://doi.org/10.1007/s44007-023-00058-xca_CA
dc.identifier.urihttp://hdl.handle.net/10234/204347
dc.description.abstractCurves are complex data. Tools for visualizing, exploring, and discovering the structure of a data set of curves are valuable. In this paper, we propose a scalable methodology to solve this challenge. On the one hand, we consider two distances in the shape and size space, one well-known distance and another recently proposed, which differentiate the contribution in shape and in size of the elements considered to compute the distance. On the other hand, we use archetypoid analysis (ADA) for the first time in elastic shape analysis. ADA is a recent technique in unsupervised statistical learning, whose objective is to find a set of archetypal observations (curves in this case), in such a way that we can describe the data set as convex combinations of these archetypal curves. This makes interpretation easy, even for non-experts. Archetypal curves or pure types are extreme cases, which also facilitates human understanding. The methodology is illustrated with a simulated data set and applied to a real problem. It is important to know the distribution of foot shapes to design suitable footwear that accommodates the population. For this purpose, we apply our proposed methodology to a real data set composed of foot contours from the adult Spanish population.ca_CA
dc.description.sponsorShipFunding for open access charge: CRUE-Universitat Jaume I
dc.format.extent24 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfLa Matematica, 2023ca_CA
dc.rights© The Author(s) 2023ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectElastic shape analysisca_CA
dc.subjectSquare-root velocity function (SRVF)ca_CA
dc.subjectArchetype analysisca_CA
dc.subjectUnsupervised machine learningca_CA
dc.subjectFootwearca_CA
dc.subjectAnthropometryca_CA
dc.titleArchetypal Curves in the Shape and Size Space: Discovering the Salient Features of Curved Big Data by Representative Extremesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s44007-023-00058-x
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s44007-023-00058-xca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameMinisterio de Ciencia e Innovaciónca_CA
project.funder.nameGeneralitat Valencianaca_CA
project.funder.nameUniversitat Jaume Ica_CA
project.funder.nameCRUE-CSIC agreement with Springer Natureca_CA
oaire.awardNumberPID2022-141699NB-I00ca_CA
oaire.awardNumberPID2020-118763GA-I00ca_CA
oaire.awardNumberPID2020-115930GA-I00ca_CA
oaire.awardNumberAICO/2021/252ca_CA
oaire.awardNumberUJI-B2020-22ca_CA


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

© The Author(s) 2023
Excepto si se señala otra cosa, la licencia del ítem se describe como: © The Author(s) 2023