{"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/local-subspace-based-outlier-detection-using","title":"Local Subspace-Based Outlier Detection using Global Neighbourhoods","arxiv_id":"1611.00183","date":"2016-11-01","proceeding":null,"authors":["Bas van Stein","Matthijs van Leeuwen","Thomas Bäck"],"abstract":"Outlier detection in high-dimensional data is a challenging yet important\ntask, as it has applications in, e.g., fraud detection and quality control.\nState-of-the-art density-based algorithms perform well because they 1) take the\nlocal neighbourhoods of data points into account and 2) consider feature\nsubspaces. In highly complex and high-dimensional data, however, existing\nmethods are likely to overlook important outliers because they do not\nexplicitly take into account that the data is often a mixture distribution of\nmultiple components.\n  We therefore introduce GLOSS, an algorithm that performs local subspace\noutlier detection using global neighbourhoods. Experiments on synthetic data\ndemonstrate that GLOSS more accurately detects local outliers in mixed data\nthan its competitors. Moreover, experiments on real-world data show that our\napproach identifies relevant outliers overlooked by existing methods,\nconfirming that one should keep an eye on the global perspective even when\ndoing local outlier detection.","url_abs":"http://arxiv.org/abs/1611.00183v1","url_pdf":"http://arxiv.org/pdf/1611.00183v1.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":"local-subspace-based-outlier-detection-using","repo_url":"https://github.com/Basvanstein/Gloss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}