JHQ: Johnson-Lindenstrauss Enhanced Hierarchical Quantization for High-Dimensional Approximate Nearest Neighbor Search
Summary: Training-free ANN quantization via orthogonal Johnson-Lindenstrauss transform: near-Gaussian, independent dimensions enable fast codebook construction with provable error bounds. JHQ adds two-level primary/residual quantization for scalable candidate filtering and refinement, yielding large index-build and query speedups on high-d ANN. (summarized by gpt-5.4-mini on May 27 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jiabao Han (Australian National University)
- 2. Mengxuan Zhang (Australian National University)
- 3. Goce Trajcevski (Iowa State University)
BibTeX Citation
@article{han_vldb26,
title = {{JHQ: Johnson-Lindenstrauss Enhanced Hierarchical Quantization for High-Dimensional Approximate Nearest Neighbor Search}},
author = {Han, Jiabao and Zhang, Mengxuan and Trajcevski, Goce},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {7},
pages = {1530--1543},
doi = {10.14778/3801059.3801067},
url = {https://doi.org/10.14778/3801059.3801067},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next