{"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/adaptive-estimation-for-approximate-k-nearest","title":"Adaptive Estimation for Approximate k-Nearest-Neighbor Computations","arxiv_id":"1902.09465","date":"2019-02-25","proceeding":null,"authors":["Daniel LeJeune","Richard G. Baraniuk","Reinhard Heckel"],"abstract":"Algorithms often carry out equally many computations for \"easy\" and \"hard\"\nproblem instances. In particular, algorithms for finding nearest neighbors\ntypically have the same running time regardless of the particular problem\ninstance. In this paper, we consider the approximate k-nearest-neighbor\nproblem, which is the problem of finding a subset of O(k) points in a given set\nof points that contains the set of k nearest neighbors of a given query point.\nWe propose an algorithm based on adaptively estimating the distances, and show\nthat it is essentially optimal out of algorithms that are only allowed to\nadaptively estimate distances. We then demonstrate both theoretically and\nexperimentally that the algorithm can achieve significant speedups relative to\nthe naive method.","url_abs":"http://arxiv.org/abs/1902.09465v1","url_pdf":"http://arxiv.org/pdf/1902.09465v1.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":"adaptive-estimation-for-approximate-k-nearest","repo_url":"https://github.com/dlej/adaptive-knn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09465","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}