ONe Index for All Kernels (ONIAK): A Zero Re-Indexing LSH Solution to ANNS-ALT (After Linear Transformation)
Summary: ONe Index for All Kernels (ONIAK) builds a universal index for ANNS-ALT across arbitrary query matrices, enabling zero re-indexing for d ≤ 200. FGoeQF tackles the dimension blowup in indexing; a Johnson-Lindenstrauss transform (JLT)-based ANNS-ALT/ALTD solution scales to large d. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jingfan Meng (Georgia Institute of Technology)
- 2. Huayi Wang (Georgia Institute of Technology)
- 3. Jun Xu (Georgia Institute of Technology)
- 4. Mitsunori Ogihara (University of Miami)
BibTeX Citation
@article{meng_vldb22,
title = {{ONe Index for All Kernels (ONIAK): A Zero Re-Indexing LSH Solution to ANNS-ALT (After Linear Transformation)}},
author = {Meng, Jingfan and Wang, Huayi and Xu, Jun and Ogihara, Mitsunori},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {13},
pages = {3937--3949},
doi = {10.14778/3565838.3565847},
url = {https://doi.org/10.14778/3565838.3565847},
year = {2022}
}
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 287 | Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search | 2007 | VLDB | 0.00022323585 |
| 332 | Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search | 2016 | VLDB | 0.00020920444 |
| 580 | SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index | 2015 | VLDB | 0.00016157635 |
| 1,546 | PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search | 2020 | VLDB | 0.00010407159 |
| 2,541 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.4500033e-05 |
| 5,121 | Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere | 2021 | SIGMOD | 6.3577317e-05 |
| 5,846 | Continuously Adaptive Similarity Search | 2020 | SIGMOD | 6.0679671e-05 |
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