{"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/self-paced-multi-task-clustering","title":"Self-Paced Multi-Task Clustering","arxiv_id":"1808.08068","date":"2018-08-24","proceeding":null,"authors":["Yazhou Ren","Xiaofan Que","Dezhong Yao","Zenglin Xu"],"abstract":"Multi-task clustering (MTC) has attracted a lot of research attentions in\nmachine learning due to its ability in utilizing the relationship among\ndifferent tasks. Despite the success of traditional MTC models, they are either\neasy to stuck into local optima, or sensitive to outliers and noisy data. To\nalleviate these problems, we propose a novel self-paced multi-task clustering\n(SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train\na series of MTC models with increasing complexity, thus highly decreases the\nrisk of trapping into poor local optima. Furthermore, to reduce the negative\ninfluence of outliers and noisy data, we design a soft version of SPMTC to\nfurther improve the clustering performance. The corresponding SPMTC framework\ncan be easily solved by an alternating optimization method. The proposed model\nis guaranteed to converge and experiments on real data sets have demonstrated\nits promising results compared with state-of-the-art multi-task clustering\nmethods.","url_abs":"http://arxiv.org/abs/1808.08068v1","url_pdf":"http://arxiv.org/pdf/1808.08068v1.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":"self-paced-multi-task-clustering","repo_url":"https://github.com/markWJJ/Multitask-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}