{"type":"ScholarlyArticle","title":"Scalable Normalized Cut with Improved Spectral Rotation","authors":[{"name":"Xiaojun Chen","slug":"xiaojun-chen","given":"Xiaojun","family":"Chen","affiliation":"College of Computer Science and Software , Shenzhen University , Shenzhen 518060 , P.R. China"},{"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":"Joshua Zhexue Huang","slug":"joshua-zhexue-huang","given":"Joshua Zhexue","family":"Huang","affiliation":"College of Computer Science and Software , Shenzhen University , Shenzhen 518060 , P.R. China"},{"name":"Min Yang","slug":"min-yang","given":"Min","family":"Yang","affiliation":"Tencent AI Lab , Shenzhen , P.R. China"}],"abstract":"Many spectral clustering algorithms have been proposed and successfully applied to many high-dimensional applications. However, there are still two problems that need to be solved: 1) existing methods for obtaining the final clustering assignments may deviate from the true discrete solution, and 2) most of these methods usually have very high computational complexity. In this paper, we propose a Scalable Normalized Cut method for clustering of large scale data. In the new method, an efficient method is used to construct a small representation matrix and then clustering is performed on the representation matrix. In the clustering process, an improved spectral rotation method is proposed to obtain the solution of the final clustering assignments. A series of experimental were conducted on 14 benchmark data sets and the experimental results show the superior performance of the new method.","doi":"10.24963/ijcai.2017/210","url":"https://ijcai.org/proceedings/2017/210","pdf":"https://ijcai.org/proceedings/2017/0210.pdf","markdown":"https://ijcai.org/proceedings/2017/210.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/210","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","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":210,"pages":{"first":1518,"last":1524},"keywords":["Machine Learning: Machine Learning","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Cai and Chen, 2015] Deng Cai and Xinlei Chen. 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