{"type":"ScholarlyArticle","title":"Doubly Aligned Incomplete Multi-view Clustering","authors":[{"name":"Menglei Hu","slug":"menglei-hu","given":"Menglei","family":"Hu","affiliation":"College of Computer Science & Technology , Nanjing University of Aeronautics & Astronautics; Collaborative Innovation Center of Novel Software Technology and Industrialization"},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":"College of Computer Science & Technology , Nanjing University of Aeronautics & Astronautics"}],"abstract":"Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of  L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.","doi":"10.24963/ijcai.2018/313","url":"https://ijcai.org/proceedings/2018/313","pdf":"https://ijcai.org/proceedings/2018/0313.pdf","markdown":"https://ijcai.org/proceedings/2018/313.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/313","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":313,"pages":{"first":2262,"last":2268},"keywords":["Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Machine Learning","Machine Learning: Clustering","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Bickel and Scheffer, 2004] Steffen Bickel and Tobias Scheffer. 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