{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-similarity-search-with-high","title":"Improving Similarity Search with High-dimensional Locality-sensitive Hashing","arxiv_id":"1812.01844","date":"2018-12-05","proceeding":null,"authors":["Jaiyam Sharma","Saket Navlakha"],"abstract":"We propose a new class of data-independent locality-sensitive hashing (LSH)\nalgorithms based on the fruit fly olfactory circuit. The fundamental difference\nof this approach is that, instead of assigning hashes as dense points in a low\ndimensional space, hashes are assigned in a high dimensional space, which\nenhances their separability. We show theoretically and empirically that this\nnew family of hash functions is locality-sensitive and preserves rank\nsimilarity for inputs in any `p space. We then analyze different variations on\nthis strategy and show empirically that they outperform existing LSH methods\nfor nearest-neighbors search on six benchmark datasets. Finally, we propose a\nmulti-probe version of our algorithm that achieves higher performance for the\nsame query time, or conversely, that maintains performance of prior approaches\nwhile taking significantly less indexing time and memory. Overall, our approach\nleverages the advantages of separability provided by high-dimensional spaces,\nwhile still remaining computationally efficient","url_abs":"http://arxiv.org/abs/1812.01844v1","url_pdf":"http://arxiv.org/pdf/1812.01844v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-similarity-search-with-high","repo_url":"https://github.com/dataplayer12/Fly-LSH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"improving-similarity-search-with-high","repo_url":"https://github.com/tian-kun/Fly-LSH","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.01844","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01844"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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