Name
Abstract | ||
|---|---|---|
This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4018/jmdem.2012100104 | INTERNATIONAL JOURNAL OF MULTIMEDIA DATA ENGINEERING & MANAGEMENT |
Keywords | Field | DocType |
Approximate Index Structure, Bregman Divergences, Experiments, Nearest Neighbor Search, Non-Metric Spaces | Space partitioning,Pattern recognition,Ball tree,Computer science,Search engine indexing,Artificial intelligence,Bregman divergence,Hyperplane,Nearest neighbor search,Speedup,Computation | Journal |
Volume | Issue | ISSN |
3 | 4 | 1947-8534 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilegsaikhan Naidan | 1 | 28 | 3.32 |
| Magnus Lie Hetland | 2 | 73 | 8.04 |