{"type":"ScholarlyArticle","title":"Self-weighted Multiview Clustering with Multiple Graphs","authors":[{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China"},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China"},{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":"Xi'an Institute of Optics and Precision Mechanics , Chinese Academy of Sciences , Xi'an 710119 , P. R. China"}],"abstract":"In multiview learning, it is essential to assign a reasonable weight to each view according to its importance. Thus, for multiview clustering task, a wise and elegant method should achieve clustering multiview data while learning the view weights. In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences. We start our work with a natural thought that the weights can be learned by introducing a hyperparameter. By analyzing the weakness of it, we further propose a new multiview clustering method which is totally self-weighted. Furthermore, once the target graph is obtained in our models, we can directly assign the cluster label to each data point and do not need any postprocessing such as $K$-means in standard spectral clustering. Evaluations on two synthetic datasets prove the effectiveness of our methods. Compared with several representative graph-based multiview clustering approaches on four real-world datasets, experimental results demonstrate that the proposed methods achieve the better performances and our new clustering method is more practical to use.","doi":"10.24963/ijcai.2017/357","url":"https://ijcai.org/proceedings/2017/357","pdf":"https://ijcai.org/proceedings/2017/0357.pdf","markdown":"https://ijcai.org/proceedings/2017/357.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/357","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra, IIIA-CSIC","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":357,"pages":{"first":2564,"last":2570},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Asuncion and Newman, 2007] Arthur Asuncion and David Newman. 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