{"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/scalable-multilabel-prediction-via-randomized","title":"Scalable Multilabel Prediction via Randomized Methods","arxiv_id":"1502.02710","date":"2015-02-09","proceeding":null,"authors":["Nikos Karampatziakis","Paul Mineiro"],"abstract":"Modeling the dependence between outputs is a fundamental challenge in\nmultilabel classification. In this work we show that a generic regularized\nnonlinearity mapping independent predictions to joint predictions is sufficient\nto achieve state-of-the-art performance on a variety of benchmark problems.\nCrucially, we compute the joint predictions without ever obtaining any\nindependent predictions, while incorporating low-rank and smoothness\nregularization. We achieve this by leveraging randomized algorithms for matrix\ndecomposition and kernel approximation. Furthermore, our techniques are\napplicable to the multiclass setting. We apply our method to a variety of\nmulticlass and multilabel data sets, obtaining state-of-the-art results.","url_abs":"http://arxiv.org/abs/1502.02710v2","url_pdf":"http://arxiv.org/pdf/1502.02710v2.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":"scalable-multilabel-prediction-via-randomized","repo_url":"https://github.com/pmineiro/randembed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"}],"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}