{"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/dartminhash-fast-sketching-for-weighted-sets","title":"DartMinHash: Fast Sketching for Weighted Sets","arxiv_id":"2005.11547","date":"2020-05-23","proceeding":null,"authors":["Tobias Christiani"],"abstract":"Weighted minwise hashing is a standard dimensionality reduction technique with applications to similarity search and large-scale kernel machines. We introduce a simple algorithm that takes a weighted set $x \\in \\mathbb{R}_{\\geq 0}^{d}$ and computes $k$ independent minhashes in expected time $O(k \\log k + \\Vert x \\Vert_{0}\\log( \\Vert x \\Vert_1 + 1/\\Vert x \\Vert_1))$, improving upon the state-of-the-art BagMinHash algorithm (KDD '18) and representing the fastest weighted minhash algorithm for sparse data. Our experiments show running times that scale better with $k$ and $\\Vert x \\Vert_0$ compared to ICWS (ICDM '10) and BagMinhash, obtaining $10$x speedups in common use cases. Our approach also gives rise to a technique for computing fully independent locality-sensitive hash values for $(L, K)$-parameterized approximate near neighbor search under weighted Jaccard similarity in optimal expected time $O(LK + \\Vert x \\Vert_0)$, improving on prior work even in the case of unweighted sets.","url_abs":"https://arxiv.org/abs/2005.11547v1","url_pdf":"https://arxiv.org/pdf/2005.11547v1.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":"dartminhash-fast-sketching-for-weighted-sets","repo_url":"https://github.com/tobc/dartminhash","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2005.11547","atlas_url":"https://app.syntology.ai/?focus=2005.11547","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}