{"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/fast-k-nearest-neighbour-search-via-dynamic","title":"Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing","arxiv_id":"1512.00442","date":"2015-12-01","proceeding":null,"authors":["Ke Li","Jitendra Malik"],"abstract":"Existing methods for retrieving k-nearest neighbours suffer from the curse of\ndimensionality. We argue this is caused in part by inherent deficiencies of\nspace partitioning, which is the underlying strategy used by most existing\nmethods. We devise a new strategy that avoids partitioning the vector space and\npresent a novel randomized algorithm that runs in time linear in dimensionality\nof the space and sub-linear in the intrinsic dimensionality and the size of the\ndataset and takes space constant in dimensionality of the space and linear in\nthe size of the dataset. The proposed algorithm allows fine-grained control\nover accuracy and speed on a per-query basis, automatically adapts to\nvariations in data density, supports dynamic updates to the dataset and is\neasy-to-implement. We show appealing theoretical properties and demonstrate\nempirically that the proposed algorithm outperforms locality-sensitivity\nhashing (LSH) in terms of approximation quality, speed and space efficiency.","url_abs":"http://arxiv.org/abs/1512.00442v3","url_pdf":"http://arxiv.org/pdf/1512.00442v3.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":"fast-k-nearest-neighbour-search-via-dynamic","repo_url":"https://github.com/dnbaker/frp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1512.00442","atlas_url":"https://app.syntology.ai/?focus=1512.00442","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}