Quality and Efficiency in High Dimensional Nearest Neighbor Search
Summary: Proposes LSB-tree, a locality-sensitive B-tree for high-dimensional NN, enabling sub-linear queries with quality guarantees in a DB-friendly index. LSB-forest stacks LSB-trees to achieve rigorous-LSH quality at much lower space and time, with linear-space updates; experiments show two orders of magnitude faster than exact NN and better quality than linear-space approximations. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yufei Tao (Chinese University of Hong Kong)
- 2. Ke Yi (Hong Kong University of Science and Technology)
- 3. Cheng Sheng (Chinese University of Hong Kong)
- 4. Panos Kalnis (King Abdullah University of Science and Technology)
BibTeX Citation
@inproceedings{tao_sigmod09,
title = {{Quality and Efficiency in High Dimensional Nearest Neighbor Search}},
author = {Tao, Yufei and Yi, Ke and Sheng, Cheng and Kalnis, Panos},
series = {{SIGMOD} '09},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1559845.1559905},
url = {https://dl.acm.org/doi/10.1145/1559845.1559905},
year = {2009}
}
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