A Fast Nearest Neighbor Method Using Empirical Marginal Distribution

Authors:
Mineichi Kudo;Jun Toyama;Hideyuki Imai
Affiliations:
Division of Computer Science Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Division of Computer Science Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Division of Computer Science Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

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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.