Abstract
Unfortunately there is no essentially faster algorithm than the brute-force algorithm for the nearest neighbor searching in high-dimensional space. The most promising way is to find an approximate nearest neighbor in high probability. This paper describes a novel algorithm that is practically faster than most of previous algorithms. Indeed, it runs in a sublinear order of the data size.
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Kudo, M., Toyama, J., Imai, H. (2009). A Fast Nearest Neighbor Method Using Empirical Marginal Distribution. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_42
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DOI: https://doi.org/10.1007/978-3-642-04592-9_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
Online ISBN: 978-3-642-04592-9
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