Name
Abstract | ||
|---|---|---|
Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1587/transinf.2015EDL8060 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
nearest neighbor search, anchor graph hashing, similarity space, principal component analysis | Locality-sensitive hashing,Graph,Pattern recognition,Locality preserving hashing,Computer science,Data dependent,Nearest neighbor graph,Artificial intelligence,Hash function,Principal component analysis,Nearest neighbor search | Journal |
Volume | Issue | ISSN |
E98D | 11 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroaki Takebe | 1 | 15 | 6.35 |
| Yusuke Uehara | 2 | 62 | 8.15 |
| Seiichi Uchida | 3 | 790 | 105.59 |