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ABSTRACT: Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational
cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined.
For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we
propose a new and simple pruning rule that can be used in most of the tree-based search algorithms. All the information needed
by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach
is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined
with other previously defined rules.
07/2007: pages 306-313;
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Pattern Recognition and Image Analysis, Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part II; 01/2007
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Pattern Recognition. 01/2006; 39:171-179.
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ABSTRACT: Nearest neighbour search is one of the most simple and used technique in Pattern Recognition.
One of the most known fast nearest neighbour algorithms was proposed by Fukunaga and Narendra. The algorithm builds a tree
in preprocess time that is traversed on search time using some elimination rules to avoid its full exploration.
This paper tests two new types of improvements in a real data environment, a spelling task. The first improvement is a new
(and faster to build) type of tree, and the second is the introduction of two new elimination rules.
Both techniques, even taken independently, reduce significantly both: the number of distance computations and the search time
expended to find the nearest neighbour.
05/2005: pages 139-152;
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Pattern Recognition and Image Analysis, Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II; 01/2005
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Progress in Pattern Recognition, Speech and Image Analysis, 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003, Havana, Cuba, November 26-29, 2003, Proceedings; 01/2003
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[show abstract]
[hide abstract]
ABSTRACT: Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms. All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules. Spanish CICIyT for partial support of this work through projects DPI2006-15542-C04-1, TIN2006-14932-C02, GV06/166, the IST Programme of the European Community, under the PASCAL Network of Excellence, IST 2002-506778.
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[show abstract]
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ABSTRACT: 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).
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[show abstract]
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ABSTRACT: A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.
Structural, Syntactic, and Statistical Pattern Recognition.
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[show abstract]
[hide abstract]
ABSTRACT: A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labelled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose and analyze a new pruning rule that only uses the information stored in a table at preprocessing time. This rule can be used alone or in combination with other known rules. An exhaustive experimentation evaluating their behavior through real and artificial data has been performed.
