{"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/hyperminhash-minhash-in-loglog-space","title":"HyperMinHash: MinHash in LogLog space","arxiv_id":"1710.08436","date":"2017-10-23","proceeding":null,"authors":["Yun William Yu","Griffin M. Weber"],"abstract":"In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size $O(\\log n)$ to buckets of size $O(\\log\\log n)$ by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash retains MinHash's features of streaming updates, unions, and cardinality estimation. For a multiplicative approximation error $1+ \\epsilon$ on a Jaccard index $ t $, given a random oracle, HyperMinHash needs $O\\left(\\epsilon^{-2} \\left( \\log\\log n + \\log \\frac{1}{ t \\epsilon} \\right)\\right)$ space. HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on the order of $10^{19}$ with relative error of around 10\\% using 64KiB of memory; MinHash can only estimate Jaccard indices for cardinalities of $10^{10}$ with the same memory consumption.","url_abs":"https://arxiv.org/abs/1710.08436v4","url_pdf":"https://arxiv.org/pdf/1710.08436v4.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"hyperminhash-minhash-in-loglog-space","repo_url":"https://github.com/yunwilliamyu/hyperminhash","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hyperminhash-minhash-in-loglog-space","repo_url":"https://github.com/LiveRamp/HyperMinHash-java","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hyperminhash-minhash-in-loglog-space","repo_url":"https://github.com/axiomhq/hyperminhash","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hyperminhash-minhash-in-loglog-space","repo_url":"https://github.com/scottlittle/drac","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}