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Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere

Summary: Introduces an asymmetric transformation enabling provable hyperplane hashing for Point-to-Hyperplane NNS beyond the unit hypersphere. NH and FH deliver sublinear queries; FH adds a data-dependent multi-partition boost, with NH favoring speed and 3–100× gains on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6193
Venue
SIGMOD
Year
2021
Pagerank
6.3577317e-05
Overall Rank
5,121 | 64.87%
DOI
10.1145/3448016.3457240

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{huang_sigmod21,
        title = {{Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere}},
        author = {Huang, Qiang and Lei, Yifan and Tung, Anthony K. H.},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457240},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457240},
        year = {2021}
}

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