{"type":"ScholarlyArticle","title":"Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering","authors":[{"name":"Junwei Han","slug":"junwei-han","given":"Junwei","family":"Han","affiliation":"Northwestern Ploytechnical University , Xi'an , 710072 , P. R. China"},{"name":"Kai Xiong","slug":"kai-xiong","given":"Kai","family":"Xiong","affiliation":"Northwestern Ploytechnical University , Xi'an , 710072 , P. R. China"},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"Northwestern Ploytechnical University , Xi'an , 710072 , P. R. China; University of Texas at Arlington , USA"}],"abstract":"Spectral clustering has been widely used due to its simplicity for solving graph clustering problem in recent years. However, it suffers from the high computational cost as data grow in scale, and is limited by the performance of post-processing. To address these two problems simultaneously, in this paper, we propose a novel approach denoted by orthogonal and nonnegative graph reconstruction (ONGR) that scales linearly with the data size. For the relaxation of Normalized Cut, we add nonnegative constraint to the objective. Due to the nonnegativity, ONGR offers interpretability that the final cluster labels can be directly obtained without post-processing. Extensive experiments on clustering tasks demonstrate the effectiveness of the proposed method.","doi":"10.24963/ijcai.2017/251","url":"https://ijcai.org/proceedings/2017/251","pdf":"https://ijcai.org/proceedings/2017/0251.pdf","markdown":"https://ijcai.org/proceedings/2017/251.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/251","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":251,"pages":{"first":1809,"last":1815},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Structured Learning","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Cai and Chen, 2014] Deng Cai and Xinlei Chen. 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