| Título: | A pruning rule based on a distance sparse table for hierarchical similarity search algorithms |
| Autores: |
Gómez Ballester, Eva Micó Andrés, Luisa Oncina Carratalá, Jose |
| Fecha: |
2009-02-19 2009-02-19 2008 2008 |
| Publicador: | Springer Berlin / Heidelberg |
| Fuente: |
Ver documento |
| Tipo: | info:eu-repo/semantics/article |
| Tema: |
Fast nearest neighbour search Metric space Tree-based algorithms Pruning rules Lenguajes y Sistemas Informáticos |
| Descripción: |
Nearest neighbour search is a simple technique widely used
in Pattern Recognition tasks. When the dataset is large and/or the dissimilarity computation is very time consuming the brute force approach
is not practical. In such cases, some properties of the dissimilarity measure can be exploited in order to speed up the search. In particular, the
metric properties of some dissimilarity measures have been used extensively in fast nearest neighbour search algorithms to avoid dissimilarity
computations. Recently, a distance table based pruning rule to reduce
the average number of distance computations in hierarchical search algorithms was proposed. In this work we show the effectiveness of this
rule compared to other state of the art algorithms. Moreover, we propose some guidelines to reduce the space complexity of the rule. Spanish CICyT for partial support of this work through projects DPI2006-15542-C04-01, the IST Programme of the European Community, under the PASCAL Network of Excellence, IST–2002-506778, and the program CONSOLIDER INGENIO 2010 (CSD2007-00018). |
| Idioma: | Inglés |
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