Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04592-9_42 | KES (2) |
Keywords | Field | DocType |
fast nearest neighbor method,sublinear order,high probability,high-dimensional space,brute-force algorithm,data size,nearest neighbor,novel algorithm,approximate nearest neighbor,previous algorithm,faster algorithm,empirical marginal distribution,nearest neighbor method,nearest neighbor search | k-nearest neighbors algorithm,Fixed-radius near neighbors,Combinatorics,Best bin first,Ball tree,Computer science,Nearest neighbor graph,Large margin nearest neighbor,Cover tree,Nearest neighbor search | Conference |
Volume | ISSN | Citations |
5712 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mineichi Kudo | 1 | 927 | 116.09 |
| Jun Toyama | 2 | 130 | 19.87 |
| Hideyuki Imai | 3 | 103 | 25.08 |