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Abstract:

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.

Registro:

Documento: Artículo
Título:Boruvka meets nearest neighbors
Autor:Tepper, M.; Musé, P.; Almansa, A.; Mejail, M.
Ciudad:Havana
Filiación:Department of Electrical and Computer Engineering, Duke University, United States
Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Uruguay
CNRS, LTCI UMR5141, Telecom. ParisTech, France
Departamento de Computación, Facultad de Ciencias Exactas Y Naturales, Universidad de Buenos Aires, Argentina
Palabras clave:General method; Generic algorithm; Large datasets; Memory consumption; Minimum spanning trees; Nearest neighbors; Orders of magnitude; Search structures; Algorithms; Computer programming; Pattern recognition
Año:2013
Volumen:8259 LNCS
Número:PART 2
Página de inicio:560
Página de fin:567
DOI: http://dx.doi.org/10.1007/978-3-642-41827-3_70
Título revista:18th Iberoamerican Congress on Pattern Recognition, CIARP 2013
Título revista abreviado:Lect. Notes Comput. Sci.
ISSN:03029743
PDF:https://bibliotecadigital.exactas.uba.ar/download/paper/paper_03029743_v8259LNCS_nPART2_p560_Tepper.pdf
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8259LNCS_nPART2_p560_Tepper

Referencias:

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  • Tepper, M., Musé, P., Almansa, A., Mejail, M., (2011) Boruvka Meets Nearest Neighbors, , Technical report, HAL: hal-00583120
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  • Samet, H., Depth-First K-Nearest Neighbor Finding Using the MaxNearestDist Estimator (2003) ICIAPA4 - Advanced Technologies Applications Center (CENATAV); International Association for Pattern Recognition (IAPR); Cuban Association for Pattern Recognition (ACRP); Cuban Society for Mathematics and Computer Sciences (SCMC); Argentine Society for Pattern Recognition (SARP-SADIO)

Citas:

---------- APA ----------
Tepper, M., Musé, P., Almansa, A. & Mejail, M. (2013) . Boruvka meets nearest neighbors. 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013, 8259 LNCS(PART 2), 560-567.
http://dx.doi.org/10.1007/978-3-642-41827-3_70
---------- CHICAGO ----------
Tepper, M., Musé, P., Almansa, A., Mejail, M. "Boruvka meets nearest neighbors" . 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 8259 LNCS, no. PART 2 (2013) : 560-567.
http://dx.doi.org/10.1007/978-3-642-41827-3_70
---------- MLA ----------
Tepper, M., Musé, P., Almansa, A., Mejail, M. "Boruvka meets nearest neighbors" . 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013, vol. 8259 LNCS, no. PART 2, 2013, pp. 560-567.
http://dx.doi.org/10.1007/978-3-642-41827-3_70
---------- VANCOUVER ----------
Tepper, M., Musé, P., Almansa, A., Mejail, M. Boruvka meets nearest neighbors. Lect. Notes Comput. Sci. 2013;8259 LNCS(PART 2):560-567.
http://dx.doi.org/10.1007/978-3-642-41827-3_70