{"type":"ScholarlyArticle","title":"Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering","authors":[{"name":"Yang Wang","slug":"yang-wang-2","given":"Yang","family":"Wang","affiliation":""},{"name":"Wenjie Zhang","slug":"wenjie-zhang","given":"Wenjie","family":"Zhang","affiliation":""},{"name":"Lin Wu","slug":"lin-wu","given":"Lin","family":"Wu","affiliation":""},{"name":"Xuemin Lin","slug":"xuemin-lin","given":"Xuemin","family":"Lin","affiliation":""},{"name":"Meng Fang","slug":"meng-fang","given":"Meng","family":"Fang","affiliation":""},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":""}],"abstract":"Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.","doi":null,"url":"https://ijcai.org/proceedings/2016/307","pdf":"https://ijcai.org/proceedings/2016/0307.pdf","markdown":"https://ijcai.org/proceedings/2016/307.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/307","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":307,"pages":{"first":2153,"last":2158},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}