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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)
- Paper ID
- 14297
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,260 | 28.63%
- DOI
-
10.14778/3801059.3801067
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Citing Paper |
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Showing 13 of 13 cited papers.
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