{"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/multi-label-learning-with-missing-labels-1","title":"Multi-label learning with missing labels using sparse global structure for label-specific features","arxiv_id":null,"date":"2023-01-24","proceeding":"Applied Intelligence Journal 2023 1","authors":["Sanjay Kumar","Nadira Ahmadi","Reshma Rastogi"],"abstract":"Multi-label learning associates a given data instance with one or several class labels. A frequent problem with real life multi-label datasets is the lack of complete label information. Incomplete labels increase model complexity as the label correlation information is not reliable, resulting in a suboptimal multi-label classifier. Further, high dimensionality of multi-label datasets often introduces spurious feature-label dependencies. Thus, discovering label-specific features is imperative for efficient handling of high-dimensional data for multi-label learning with missing labels. To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data. Instances are expressed as linear combination of label-specific features and the inter-relation guides the construction of model coefficients. The model also constructs supplementary label correlations to assist missing label recovery as part of the optimization problem. Empirical results on benchmark multi-label datasets highlight the effectiveness of the proposed method.","url_abs":"https://link.springer.com/article/10.1007/s10489-022-04439-7","url_pdf":"https://doi.org/10.1007/s10489-022-04439-7","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":"multi-label-learning-with-missing-labels-1","repo_url":"https://github.com/ml-lab-sau/Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"missing-labels","task_name":"Missing Labels"},{"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}