Quaternion kernel partial least squares regression algorithms.

This work provides three quaternion kernel partial least squares (PLS) algorithms for linear and nonlinear regressions. Firstly, the problem of large ill-conditioned matrices is tackled and two specifically designed linear kernel algorithms are suggested. Secondly, since PLS can present low regressi...

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Detalhes bibliográficos
Autores: Jiménez-López, José Domingo, Fernández-Alcalá, Rosa María, Navarro-Moreno, Jesús, Ruiz-Molina, Juan Carlos
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/6338
Acesso em linha:https://hdl.handle.net/10953/6338
Access Level:acceso abierto
Palavra-chave:Ill-conditioned matrices
Linear and nonlinear regression models
Partial least squares
Quaternion kernel methods
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spelling Quaternion kernel partial least squares regression algorithms.Jiménez-López, José DomingoFernández-Alcalá, Rosa MaríaNavarro-Moreno, JesúsRuiz-Molina, Juan CarlosIll-conditioned matricesLinear and nonlinear regression modelsPartial least squaresQuaternion kernel methodsN/AThis work provides three quaternion kernel partial least squares (PLS) algorithms for linear and nonlinear regressions. Firstly, the problem of large ill-conditioned matrices is tackled and two specifically designed linear kernel algorithms are suggested. Secondly, since PLS can present low regression accuracy and prediction performance for nonlinear data, a kernel algorithm for performing quaternion nonlinear regression is also given. Computational results and discussion illustrate the relative merits of the algorithms proposed over closely related regression methodsElsevier202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10953/6338reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésJournal of the Franklin InstituteAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/63382026-06-24T12:41:07Z
dc.title.none.fl_str_mv Quaternion kernel partial least squares regression algorithms.
title Quaternion kernel partial least squares regression algorithms.
spellingShingle Quaternion kernel partial least squares regression algorithms.
Jiménez-López, José Domingo
Ill-conditioned matrices
Linear and nonlinear regression models
Partial least squares
Quaternion kernel methods
N/A
title_short Quaternion kernel partial least squares regression algorithms.
title_full Quaternion kernel partial least squares regression algorithms.
title_fullStr Quaternion kernel partial least squares regression algorithms.
title_full_unstemmed Quaternion kernel partial least squares regression algorithms.
title_sort Quaternion kernel partial least squares regression algorithms.
dc.creator.none.fl_str_mv Jiménez-López, José Domingo
Fernández-Alcalá, Rosa María
Navarro-Moreno, Jesús
Ruiz-Molina, Juan Carlos
author Jiménez-López, José Domingo
author_facet Jiménez-López, José Domingo
Fernández-Alcalá, Rosa María
Navarro-Moreno, Jesús
Ruiz-Molina, Juan Carlos
author_role author
author2 Fernández-Alcalá, Rosa María
Navarro-Moreno, Jesús
Ruiz-Molina, Juan Carlos
author2_role author
author
author
dc.subject.none.fl_str_mv Ill-conditioned matrices
Linear and nonlinear regression models
Partial least squares
Quaternion kernel methods
N/A
topic Ill-conditioned matrices
Linear and nonlinear regression models
Partial least squares
Quaternion kernel methods
N/A
description This work provides three quaternion kernel partial least squares (PLS) algorithms for linear and nonlinear regressions. Firstly, the problem of large ill-conditioned matrices is tackled and two specifically designed linear kernel algorithms are suggested. Secondly, since PLS can present low regression accuracy and prediction performance for nonlinear data, a kernel algorithm for performing quaternion nonlinear regression is also given. Computational results and discussion illustrate the relative merits of the algorithms proposed over closely related regression methods
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10953/6338
url https://hdl.handle.net/10953/6338
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of the Franklin Institute
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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