{"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/identifying-and-alleviating-concept-drift-in","title":"Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition","arxiv_id":"1804.09619","date":"2018-04-25","proceeding":null,"authors":["Ravdeep Pasricha","Ekta Gujral","Evangelos E. Papalexakis"],"abstract":"Tensor decompositions are used in various data mining applications from\nsocial network to medical applications and are extremely useful in discovering\nlatent structures or concepts in the data. Many real-world applications are\ndynamic in nature and so are their data. To deal with this dynamic nature of\ndata, there exist a variety of online tensor decomposition algorithms. A\ncentral assumption in all those algorithms is that the number of latent\nconcepts remains fixed throughout the entire stream. However, this need not be\nthe case. Every incoming batch in the stream may have a different number of\nlatent concepts, and the difference in latent concepts from one tensor batch to\nanother can provide insights into how our findings in a particular application\nbehave and deviate over time. In this paper, we define \"concept\" and \"concept\ndrift\" in the context of streaming tensor decomposition, as the manifestation\nof the variability of latent concepts throughout the stream. Furthermore, we\nintroduce SeekAndDestroy, an algorithm that detects concept drift in streaming\ntensor decomposition and is able to produce results robust to that drift. To\nthe best of our knowledge, this is the first work that investigates concept\ndrift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy\non synthetic datasets, which exhibit a wide variety of realistic drift. Our\nexperiments demonstrate the effectiveness of SeekAndDestroy, both in the\ndetection of concept drift and in the alleviation of its effects, producing\nresults with similar quality to decomposing the entire tensor in one shot.\nAdditionally, in real datasets, SeekAndDestroy outperforms other streaming\nbaselines, while discovering novel useful components.","url_abs":"http://arxiv.org/abs/1804.09619v2","url_pdf":"http://arxiv.org/pdf/1804.09619v2.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":"identifying-and-alleviating-concept-drift-in","repo_url":"https://github.com/ravdeep003/conceptDrift","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"tensor-decomposition","task_name":"Tensor Decomposition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}