MQH: Locality Sensitive Hashing on Multi-level Quantization Errors for Point-to-Hyperplane Distances
Summary: Introduces MQH: an LSH for point-to-hyperplane NN that hashes multi-level quantization residuals from a stepwise quantizer to capture distance-to-hyperplane signals. Adaptive per-query level selection and error-based bucket sizing give provable guarantees and 2×–10× speedups over prior LSH. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kejing Lu
- 2. Yoshiharu Ishikawa
- 3. Chuan Xiao
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,000 | A New Sparse Data Clustering Method Based On Frequent Items | 2023 | SIGMOD | 5.2365238e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 579 | Locality-Sensitive Hashing Scheme Based on Dynamic Collision Counting | 2012 | SIGMOD | 0.0001982328 |
| 1,934 | VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning | 2020 | VLDB | 0.00010047294 |
| 2,687 | HVS: Hierarchical Graph Structure Based on Voronoi Diagrams for Solving Approximate Nearest Neighbor Search | 2022 | VLDB | 8.3079951e-05 |
| 4,869 | Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere | 2021 | SIGMOD | 5.8588434e-05 |
Previous
Page 1 / 1
Next