Boruvka meets nearest neighbors
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 distanc...
Guardado en:
| Autores principales: | , |
|---|---|
| Publicado: |
2013
|
| Materias: | |
| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v8259LNCS_nPART2_p560_Tepper http://hdl.handle.net/20.500.12110/paper_03029743_v8259LNCS_nPART2_p560_Tepper |
| Aporte de: |
| Sumario: | 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. |
|---|