VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning
Summary: VHP bounds LSH candidate search with a query-centered virtual hypersphere, emulated by coordinated physical hyperspheres across projections. Independent B+-trees and principled radius growth provide probabilistic c-ANN guarantees and up to 2× speedups on billion-scale data. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Kejing Lu (Hokkaido University)
- 2. Hongya Wang (Donghua University)
- 3. Wei Wang (University of New South Wales)
- 4. Mineichi Kudo (Hokkaido University)
BibTeX Citation
@article{lu_vldb20,
title = {{VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning}},
author = {Lu, Kejing and Wang, Hongya and Wang, Wei and Kudo, Mineichi},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {9},
pages = {1443--1455},
doi = {10.14778/3397230.3397240},
url = {https://doi.org/10.14778/3397230.3397240},
year = {2020}
}
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