Boruvka meets nearest neighbors

Autores
Tepper, M.; Musé, P.; Almansa, A.; Mejail, M.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Computing the minimum spanning tree (MST) is a common task in the pattern recognition and the computer vision fields. However, little work has been done on efficient general methods for solving the problem on large datasets where graphs are complete and edge weights are given implicitly by a distance between vertex attributes. In this work we propose a generic algorithm that extends the classical Boruvka's algorithm by using nearest neighbors search structures to significantly reduce time and memory consumption. The algorithm can also compute in a straightforward way approximate MSTs thus further improving speed. Experiments show that the proposed method outperforms classical algorithms on large low-dimensional datasets by several orders of magnitude. © Springer-Verlag 2013.
Fil:Tepper, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
Lect. Notes Comput. Sci. 2013;8259 LNCS(PART 2):560-567
Materia
General method
Generic algorithm
Large datasets
Memory consumption
Minimum spanning trees
Nearest neighbors
Orders of magnitude
Search structures
Algorithms
Computer programming
Pattern recognition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_03029743_v8259LNCS_nPART2_p560_Tepper

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repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Boruvka meets nearest neighborsTepper, M.Musé, P.Almansa, A.Mejail, M.General methodGeneric algorithmLarge datasetsMemory consumptionMinimum spanning treesNearest neighborsOrders of magnitudeSearch structuresAlgorithmsComputer programmingPattern recognitionComputing the minimum spanning tree (MST) is a common task in the pattern recognition and the computer vision fields. However, little work has been done on efficient general methods for solving the problem on large datasets where graphs are complete and edge weights are given implicitly by a distance between vertex attributes. In this work we propose a generic algorithm that extends the classical Boruvka's algorithm by using nearest neighbors search structures to significantly reduce time and memory consumption. The algorithm can also compute in a straightforward way approximate MSTs thus further improving speed. Experiments show that the proposed method outperforms classical algorithms on large low-dimensional datasets by several orders of magnitude. © Springer-Verlag 2013.Fil:Tepper, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_03029743_v8259LNCS_nPART2_p560_TepperLect. Notes Comput. Sci. 2013;8259 LNCS(PART 2):560-567reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-29T13:42:58Zpaperaa:paper_03029743_v8259LNCS_nPART2_p560_TepperInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:42:59.301Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Boruvka meets nearest neighbors
title Boruvka meets nearest neighbors
spellingShingle Boruvka meets nearest neighbors
Tepper, M.
General method
Generic algorithm
Large datasets
Memory consumption
Minimum spanning trees
Nearest neighbors
Orders of magnitude
Search structures
Algorithms
Computer programming
Pattern recognition
title_short Boruvka meets nearest neighbors
title_full Boruvka meets nearest neighbors
title_fullStr Boruvka meets nearest neighbors
title_full_unstemmed Boruvka meets nearest neighbors
title_sort Boruvka meets nearest neighbors
dc.creator.none.fl_str_mv Tepper, M.
Musé, P.
Almansa, A.
Mejail, M.
author Tepper, M.
author_facet Tepper, M.
Musé, P.
Almansa, A.
Mejail, M.
author_role author
author2 Musé, P.
Almansa, A.
Mejail, M.
author2_role author
author
author
dc.subject.none.fl_str_mv General method
Generic algorithm
Large datasets
Memory consumption
Minimum spanning trees
Nearest neighbors
Orders of magnitude
Search structures
Algorithms
Computer programming
Pattern recognition
topic General method
Generic algorithm
Large datasets
Memory consumption
Minimum spanning trees
Nearest neighbors
Orders of magnitude
Search structures
Algorithms
Computer programming
Pattern recognition
dc.description.none.fl_txt_mv Computing the minimum spanning tree (MST) is a common task in the pattern recognition and the computer vision fields. However, little work has been done on efficient general methods for solving the problem on large datasets where graphs are complete and edge weights are given implicitly by a distance between vertex attributes. In this work we propose a generic algorithm that extends the classical Boruvka's algorithm by using nearest neighbors search structures to significantly reduce time and memory consumption. The algorithm can also compute in a straightforward way approximate MSTs thus further improving speed. Experiments show that the proposed method outperforms classical algorithms on large low-dimensional datasets by several orders of magnitude. © Springer-Verlag 2013.
Fil:Tepper, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description Computing the minimum spanning tree (MST) is a common task in the pattern recognition and the computer vision fields. However, little work has been done on efficient general methods for solving the problem on large datasets where graphs are complete and edge weights are given implicitly by a distance between vertex attributes. In this work we propose a generic algorithm that extends the classical Boruvka's algorithm by using nearest neighbors search structures to significantly reduce time and memory consumption. The algorithm can also compute in a straightforward way approximate MSTs thus further improving speed. Experiments show that the proposed method outperforms classical algorithms on large low-dimensional datasets by several orders of magnitude. © Springer-Verlag 2013.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_03029743_v8259LNCS_nPART2_p560_Tepper
url http://hdl.handle.net/20.500.12110/paper_03029743_v8259LNCS_nPART2_p560_Tepper
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Lect. Notes Comput. Sci. 2013;8259 LNCS(PART 2):560-567
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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