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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brin, S.: Near neighbor search in large metric spaces. In: Proceedings of the 21st VLDB Conference, pp. 574–584 (1995)
Chávez, E., Navarro, G., Baeza-Yates, R., Marroquin, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)
Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)
Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3, 209–226 (1977)
Fukunaga, K., Narendra, P.M.: A branch and bound algorithm for computing k-nearest neighbors. IEEE Transactions on Computers, IEC 24, 750–753 (1975)
Gómez-Ballester, E., Micó, L., Oncina, J.: Some improvements in tree based nearest neighbour search algorithms. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 456–463. Springer, Heidelberg (2003)
Gómez-Ballester, E., Micó, L., Oncina, J.: Some approaches to improve tree-based nearest neighbour search algorithms. Pattern Recognition 39(2), 171–179 (2006)
McNames, J.: A fast nearest neighbor algorithm based on a principal axis tree. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 964–976 (2001)
Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, pp. 311–321 (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Oncina, J., Thollard, F., Gómez-Ballester, E., Micó, L., Moreno-Seco, F. (2007). A Tabular Pruning Rule in Tree-Based Fast Nearest Neighbor Search Algorithms. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-72849-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
Online ISBN: 978-3-540-72849-8
eBook Packages: Computer ScienceComputer Science (R0)