A Tabular Pruning Rule in Tree-Based Fast Nearest Neighbor Search Algorithms

Authors:
Jose Oncina;Franck Thollard;Eva Gómez-Ballester;Luisa Micó;Francisco Moreno-Seco
Affiliations:
Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain;Laboratoire Hubert Curien (ex EURISE) - UMR CNRS 5516, 18 rue du Prof. Lauras - 42000 Saint-Étienne Cedex 2, France;Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain;Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain;Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Year:
2007

Citing 8
Cited 3

Data structures and algorithms for nearest neighbor search in general metric spaces

SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time

ACM Transactions on Mathematical Software (TOMS)
Searching in metric spaces

ACM Computing Surveys (CSUR)
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree

IEEE Transactions on Pattern Analysis and Machine Intelligence
Near Neighbor Search in Large Metric Spaces

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Some approaches to improve tree-based nearest neighbour search algorithms

Pattern Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors

IEEE Transactions on Computers

Fast k Most Similar Neighbor Classifier for Mixed Data Based on a Tree Structure and Approximating-Eliminating

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Combining elimination rules in tree-based nearest neighbor search algorithms

SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition

Quantified Score

Hi-index	0.00

Visualization

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.