{"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/iterative-universal-hash-function-generator","title":"Iterative Universal Hash Function Generator for Minhashing","arxiv_id":"1401.6124","date":"2014-01-23","proceeding":null,"authors":["Fabricio Olivetti de Franca"],"abstract":"Minhashing is a technique used to estimate the Jaccard Index between two sets\nby exploiting the probability of collision in a random permutation. In order to\nspeed up the computation, a random permutation can be approximated by using an\nuniversal hash function such as the $h_{a,b}$ function proposed by Carter and\nWegman. A better estimate of the Jaccard Index can be achieved by using many of\nthese hash functions, created at random. In this paper a new iterative\nprocedure to generate a set of $h_{a,b}$ functions is devised that eliminates\nthe need for a list of random values and avoid the multiplication operation\nduring the calculation. The properties of the generated hash functions remains\nthat of an universal hash function family. This is possible due to the random\nnature of features occurrence on sparse datasets. Results show that the\nuniformity of hashing the features is maintaned while obtaining a speed up of\nup to $1.38$ compared to the traditional approach.","url_abs":"http://arxiv.org/abs/1401.6124v1","url_pdf":"http://arxiv.org/pdf/1401.6124v1.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":"iterative-universal-hash-function-generator","repo_url":"https://github.com/folivetti/HBLCoClust","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}