Optimum-Path Forest based on k-connectivity: Theory and applications
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.patrec.2016.07.026 http://hdl.handle.net/11449/162543 |
Resumo: | Graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved. |
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Repositório Institucional da UNESP |
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Optimum-Path Forest based on k-connectivity: Theory and applicationsPattern classificationOptimum-Path ForestSupervised learningGraph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube, BR-17033360 Bauru, SP, BrazilUniv Fed Sao Carlos, Dept Comp, Rod Washington Luis,Km 235, BR-13565905 Sao Carlos, SP, BrazilUniv Estadual Campinas, Inst Comp, Av Albert Einstein 1251, BR-13083852 Campinas, SP, BrazilSao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube, BR-17033360 Bauru, SP, BrazilCAPES: 2966/2014FAPESP: 2009/16206-1FAPESP: 2013/20387-7FAPESP: 2014/2014/16250-9CNPq: 303182/2011-3CNPq: 70571/2013-6CNPq: 306166/2014-3Elsevier B.V.Universidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Universidade Estadual de Campinas (UNICAMP)Papa, Joao Paulo [UNESP]Nachif Fernandes, Silas EvandroFalcao, Alexandre Xavier2018-11-26T17:20:52Z2018-11-26T17:20:52Z2017-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article117-126application/pdfhttp://dx.doi.org/10.1016/j.patrec.2016.07.026Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 87, p. 117-126, 2017.0167-8655http://hdl.handle.net/11449/16254310.1016/j.patrec.2016.07.026WOS:000395616700015WOS000395616700015.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPattern Recognition Letters0,662info:eu-repo/semantics/openAccess2024-04-23T16:10:48Zoai:repositorio.unesp.br:11449/162543Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:10:13.733922Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimum-Path Forest based on k-connectivity: Theory and applications |
title |
Optimum-Path Forest based on k-connectivity: Theory and applications |
spellingShingle |
Optimum-Path Forest based on k-connectivity: Theory and applications Papa, Joao Paulo [UNESP] Pattern classification Optimum-Path Forest Supervised learning |
title_short |
Optimum-Path Forest based on k-connectivity: Theory and applications |
title_full |
Optimum-Path Forest based on k-connectivity: Theory and applications |
title_fullStr |
Optimum-Path Forest based on k-connectivity: Theory and applications |
title_full_unstemmed |
Optimum-Path Forest based on k-connectivity: Theory and applications |
title_sort |
Optimum-Path Forest based on k-connectivity: Theory and applications |
author |
Papa, Joao Paulo [UNESP] |
author_facet |
Papa, Joao Paulo [UNESP] Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier |
author_role |
author |
author2 |
Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Carlos (UFSCar) Universidade Estadual de Campinas (UNICAMP) |
dc.contributor.author.fl_str_mv |
Papa, Joao Paulo [UNESP] Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier |
dc.subject.por.fl_str_mv |
Pattern classification Optimum-Path Forest Supervised learning |
topic |
Pattern classification Optimum-Path Forest Supervised learning |
description |
Graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-02-01 2018-11-26T17:20:52Z 2018-11-26T17:20:52Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.patrec.2016.07.026 Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 87, p. 117-126, 2017. 0167-8655 http://hdl.handle.net/11449/162543 10.1016/j.patrec.2016.07.026 WOS:000395616700015 WOS000395616700015.pdf |
url |
http://dx.doi.org/10.1016/j.patrec.2016.07.026 http://hdl.handle.net/11449/162543 |
identifier_str_mv |
Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 87, p. 117-126, 2017. 0167-8655 10.1016/j.patrec.2016.07.026 WOS:000395616700015 WOS000395616700015.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pattern Recognition Letters 0,662 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
117-126 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128904270970880 |