Boruvka meets nearest neighbors
Resumen:
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.
| 2013 | |
| Procesamiento de Señales | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41779 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522938863222784 |
|---|---|
| author | Tepper, Mariano |
| author2 | Musé, Pablo Almansa, Andrés Mejail, Marta |
| author2_role | author author author |
| author_facet | Tepper, Mariano Musé, Pablo Almansa, Andrés Mejail, Marta |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Tepper, Mariano Musé, Pablo Almansa, Andrés Mejail, Marta |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:44Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:44Z |
| dc.date.issued.es.fl_str_mv | 2013 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.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. |
| dc.description.es.fl_txt_mv | Trabajo presentado a CIARP 2013: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. |
| dc.identifier.citation.es.fl_str_mv | Tepper, M., Musé, P., Almansa, A., Mejail, M. "Boruvka meets nearest neighbors". Publicado en: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_70 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41779 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Boruvka meets nearest neighbors |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado a CIARP 2013: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_2244dec38d07774ae456f3e533eabc0a |
| identifier_str_mv | Tepper, M., Musé, P., Almansa, A., Mejail, M. "Boruvka meets nearest neighbors". Publicado en: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_70 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41779 |
| publishDate | 2013 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:44Z2023-12-11T19:57:44Z201320231211Tepper, M., Musé, P., Almansa, A., Mejail, M. "Boruvka meets nearest neighbors". Publicado en: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_70https://hdl.handle.net/20.500.12008/41779Trabajo presentado a CIARP 2013: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications.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.Made available in DSpace on 2023-12-11T19:57:44Z (GMT). No. of bitstreams: 5 TMAM13.pdf: 449953 bytes, checksum: 3545fc9fa94e03f288481767000c4e69 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2013enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Procesamiento de SeñalesBoruvka meets nearest neighborsPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaTepper, MarianoMusé, PabloAlmansa, AndrésMejail, MartaProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Boruvka meets nearest neighbors Tepper, Mariano Procesamiento de Señales |
| status_str | publishedVersion |
| title | Boruvka meets nearest neighbors |
| title_full | Boruvka meets nearest neighbors |
| title_fullStr | Boruvka meets nearest neighbors |
| title_full_unstemmed | Boruvka meets nearest neighbors |
| title_short | Boruvka meets nearest neighbors |
| title_sort | Boruvka meets nearest neighbors |
| topic | Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41779 |
