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Permutation Search Methods are Efficient, Yet Faster Search is Possible

Summary: Permutation-based kNN uses pivot-ranked permutations as distance proxies for metric and non-metric spaces. Evaluated against multi-probe LSH, VP-tree, and proximity-graph baselines on large in-memory image/text data, permutation methods show reasonable accuracy and efficiency; code and data released. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11228
Venue
VLDB
Year
2015
Pagerank
6.4197336e-05
Overall Rank
4,970 | 65.91%
DOI
10.14778/2824032.2824059

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{naidan_vldb15,
        title = {{Permutation Search Methods are Efficient, Yet Faster Search is Possible}},
        author = {Naidan, Bilegsaikhan and Boytsov, Leonid and Nyberg, Eric},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {12},
        pages = {1618--1629},
        doi = {10.14778/2824032.2824059},
        url = {https://doi.org/10.14778/2824032.2824059},
        year = {2015}
}

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