{"type":"ScholarlyArticle","title":"Multi-Task Clustering with Model Relation Learning","authors":[{"name":"Xiaotong Zhang","slug":"xiaotong-zhang","given":"Xiaotong","family":"Zhang","affiliation":"School of Software , Dalian University of Technology; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"},{"name":"Xianchao Zhang","slug":"xianchao-zhang","given":"Xianchao","family":"Zhang","affiliation":"School of Software , Dalian University of Technology; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"},{"name":"Han Liu","slug":"han-liu","given":"Han","family":"Liu","affiliation":"School of Software , Dalian University of Technology; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"},{"name":"Jiebo Luo","slug":"jiebo-luo","given":"Jiebo","family":"Luo","affiliation":"Department of Computer Science , University of Rochester"}],"abstract":"Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.","doi":"10.24963/ijcai.2018/435","url":"https://ijcai.org/proceedings/2018/435","pdf":"https://ijcai.org/proceedings/2018/0435.pdf","markdown":"https://ijcai.org/proceedings/2018/435.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/435","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":435,"pages":{"first":3132,"last":3140},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Al-Stouhi and Reddy, 2014] Samir Al-Stouhi and Chan- dan K. 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