{"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/an-online-algorithm-for-nonparametric","title":"An Online Algorithm for Nonparametric Correlations","arxiv_id":"1712.01521","date":"2017-12-05","proceeding":null,"authors":["Wei Xiao"],"abstract":"Nonparametric correlations such as Spearman's rank correlation and Kendall's\ntau correlation are widely applied in scientific and engineering fields. This\npaper investigates the problem of computing nonparametric correlations on the\nfly for streaming data. Standard batch algorithms are generally too slow to\nhandle real-world big data applications. They also require too much memory\nbecause all the data need to be stored in the memory before processing. This\npaper proposes a novel online algorithm for computing nonparametric\ncorrelations. The algorithm has O(1) time complexity and O(1) memory cost and\nis quite suitable for edge devices, where only limited memory and processing\npower are available. You can seek a balance between speed and accuracy by\nchanging the number of cutpoints specified in the algorithm. The online\nalgorithm can compute the nonparametric correlations 10 to 1,000 times faster\nthan the corresponding batch algorithm, and it can compute them based either on\nall past observations or on fixed-size sliding windows.","url_abs":"http://arxiv.org/abs/1712.01521v1","url_pdf":"http://arxiv.org/pdf/1712.01521v1.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":"an-online-algorithm-for-nonparametric","repo_url":"https://github.com/wxiao0421/onlineNPCORR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-summarization","task_name":"Data Summarization"},{"task_slug":"sequential-correlation-estimation","task_name":"Sequential Correlation Estimation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}