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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Kudo, M., Toyama, J., & Imai, H. (2009). A fast nearest neighbor method using empirical marginal distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5712 LNAI, pp. 333–339). https://doi.org/10.1007/978-3-642-04592-9_42
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