{"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/outlier-detection-for-text-data-an-extended","title":"Outlier Detection for Text Data : An Extended Version","arxiv_id":"1701.01325","date":"2017-01-05","proceeding":null,"authors":["Ramakrishnan Kannan","Hyenkyun Woo","Charu C. Aggarwal","Haesun Park"],"abstract":"The problem of outlier detection is extremely challenging in many domains\nsuch as text, in which the attribute values are typically non-negative, and\nmost values are zero. In such cases, it often becomes difficult to separate the\noutliers from the natural variations in the patterns in the underlying data. In\nthis paper, we present a matrix factorization method, which is naturally able\nto distinguish the anomalies with the use of low rank approximations of the\nunderlying data. Our iterative algorithm TONMF is based on block coordinate\ndescent (BCD) framework. We define blocks over the term-document matrix such\nthat the function becomes solvable. Given most recently updated values of other\nmatrix blocks, we always update one block at a time to its optimal. Our\napproach has significant advantages over traditional methods for text outlier\ndetection. Finally, we present experimental results illustrating the\neffectiveness of our method over competing methods.","url_abs":"http://arxiv.org/abs/1701.01325v1","url_pdf":"http://arxiv.org/pdf/1701.01325v1.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":"outlier-detection-for-text-data-an-extended","repo_url":"https://github.com/ramkikannan/outliernmf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}