Abstract
In this paper we show the results of a performance comparison between two Nearest Neighbour Search Methods: one, proposed by Arya & Mount, is based on a kd–tree data structure and a Branch and Bound approximate search algorithm [1], and the other is a search method based on dimensionality projections, presented by Nene & Nayar in [5]. A number of experiments have been carried out in order to find the best choice to work with high dimensional points and large data sets.
Work partially supported by Spanish CICYT under grant TIC 2003-08496-C04-02.
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Cano, J., Pérez-Cortés, JC., Salvador, I. (2004). Comparison of Two Fast Nearest-Neighbour Search Methods in High-Dimensional Large-Sized Databases. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_95
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DOI: https://doi.org/10.1007/978-3-540-27868-9_95
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