Name
Abstract | ||
|---|---|---|
We present an approximation technique for similarity queries in metric spaces seeking to reduce the number of distance computations required to process the query. The new technique could be applied to ball and hyperplane regions in metric index structures, but also to non-metric index structures such as generalized search trees. Experimental results show that our sampling based technique performed better than the relative error approximation technique, but worse than the region proximity technique. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1995412.1995439 | SISAP |
Keywords | Field | DocType |
approximation technique,hyperplane region,region proximity technique,generalized search tree,approximate similarity search,metric space,index structure,relative error approximation technique,metric index structure,new technique,meta search engine,similarity search,relative error | Artificial intelligence,Hyperplane,Nearest neighbor search,Computation,Discrete mathematics,Metasearch engine,Metric tree,Algorithm,Sampling (statistics),Metric space,Mathematics,Machine learning,Approximation error | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilegsaikhan Naidan | 1 | 28 | 3.32 |
| Magnus Lie Hetland | 2 | 73 | 8.04 |
| Ole Edsberg | 3 | 51 | 4.13 |