DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search
文献类型：期刊论文
| 作者 | Wei, Jiuqi1,2; Peng, Botao1; Lee, Xiaodong1; Palpanas, Themis3 |
| 刊名 | PROCEEDINGS OF THE VLDB ENDOWMENT
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 17期号:9页码:2241-2254 |
| ISSN号 | 2150-8097 |
| DOI | 10.14778/3665844.3665854 |
| 英文摘要 | Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query accuracy. Traditional LSH-based methods mainly focus on improving the efficiency and accuracy of the query phase by designing different query strategies, but pay little attention to improving the efficiency of the indexing phase. They typically fine-tune existing data-oriented partitioning trees to index data points and support their query strategies. However, their strategy to directly partition the multidimensional space is time-consuming, and performance degrades as the space dimensionality increases. In this paper, we design an encoding-based tree called Dynamic Encoding Tree (DE-Tree) to improve the indexing efficiency and support efficient range queries based on Euclidean distance. Based on DE-Tree, we propose a novel LSH scheme called DET-LSH. DET-LSH adopts a novel query strategy, which performs range queries in multiple independent index DE-Trees to reduce the probability of missing exact NN points, thereby improving the query accuracy. Our theoretical studies show that DET-LSH enjoys probabilistic guarantees on query accuracy. Extensive experiments on real-world datasets demonstrate the superiority of DET-LSH over the state-of-the-art LSH-based methods on both efficiency and accuracy. While achieving better query accuracy than competitors, DET-LSH achieves up to 6x speedup in indexing time and 2x speedup in query time over the state-of-the-art LSH-based methods. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[62202450] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001308222700010 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39593]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wei, Jiuqi |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Univ Paris Cite, LIPADE, Paris, France |
| 推荐引用方式 GB/T 7714 | Wei, Jiuqi,Peng, Botao,Lee, Xiaodong,et al. DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search[J]. PROCEEDINGS OF THE VLDB ENDOWMENT,2024,17(9):2241-2254. |
| APA | Wei, Jiuqi,Peng, Botao,Lee, Xiaodong,&Palpanas, Themis.(2024).DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search.PROCEEDINGS OF THE VLDB ENDOWMENT,17(9),2241-2254. |
| MLA | Wei, Jiuqi,et al."DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search".PROCEEDINGS OF THE VLDB ENDOWMENT 17.9(2024):2241-2254. |
入库方式： OAI收割
来源：计算技术研究所
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