MQH: Locality Sensitive Hashing on Multi-level Quantization Errors for Point-to-Hyperplane Distances
Summary: MQH proposes provable LSH for point-to-hyperplane NNS using residuals from multi-level, stepwise quantization. Query-adaptive levels and error-dependent bucket sizes sharpen pruning, delivering 2–10× speedups over prior LSH methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kejing Lu (Nagoya University)
- 2. Yoshiharu Ishikawa (Nagoya University)
- 3. Chuan Xiao (Osaka University)
BibTeX Citation
@article{lu_vldb23,
title = {{MQH: Locality Sensitive Hashing on Multi-level Quantization Errors for Point-to-Hyperplane Distances}},
author = {Lu, Kejing and Ishikawa, Yoshiharu and Xiao, Chuan},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {864--876},
doi = {10.14778/3574245.3574269},
url = {https://doi.org/10.14778/3574245.3574269},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,956 | A New Sparse Data Clustering Method Based On Frequent Items | 2023 | SIGMOD | 5.7303405e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 369 | Locality-Sensitive Hashing Scheme Based on Dynamic Collision Counting | 2012 | SIGMOD | 0.00019945234 |
| 1,430 | VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning | 2020 | VLDB | 0.0001080902 |
| 1,802 | HVS: Hierarchical Graph Structure Based on Voronoi Diagrams for Solving Approximate Nearest Neighbor Search | 2022 | VLDB | 9.7284341e-05 |
| 5,121 | Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere | 2021 | SIGMOD | 6.3577317e-05 |
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