{"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/deep-extreme-multi-label-learning","title":"Deep Extreme Multi-label Learning","arxiv_id":"1704.03718","date":"2017-04-12","proceeding":null,"authors":["Wenjie Zhang","Junchi Yan","Xiangfeng Wang","Hongyuan Zha"],"abstract":"Extreme multi-label learning (XML) or classification has been a practical and\nimportant problem since the boom of big data. The main challenge lies in the\nexponential label space which involves $2^L$ possible label sets especially\nwhen the label dimension $L$ is huge, e.g., in millions for Wikipedia labels.\nThis paper is motivated to better explore the label space by originally\nestablishing an explicit label graph. In the meanwhile, deep learning has been\nwidely studied and used in various classification problems including\nmulti-label classification, however it has not been properly introduced to XML,\nwhere the label space can be as large as in millions. In this paper, we propose\na practical deep embedding method for extreme multi-label classification, which\nharvests the ideas of non-linear embedding and graph priors-based label space\nmodeling simultaneously. Extensive experiments on public datasets for XML show\nthat our method performs competitive against state-of-the-art result.","url_abs":"http://arxiv.org/abs/1704.03718v4","url_pdf":"http://arxiv.org/pdf/1704.03718v4.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":"deep-extreme-multi-label-learning","repo_url":"https://github.com/theGuyWithBlackTie/Deep-Extreme-Multi-Label-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"extreme-multi-label-classification","task_name":"Extreme Multi-Label Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}