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
- 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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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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13.070432 |