{"type":"ScholarlyArticle","title":"Semi-supervised Clustering via Pairwise Constrained Optimal Graph","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 , Shaanxi , P. R. China"},{"name":"Han Zhang","slug":"han-zhang","given":"Han","family":"Zhang","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an , 710072 , Shaanxi , P. R. China"},{"name":"Rong Wang","slug":"rong-wang","given":"Rong","family":"Wang","affiliation":"School of Cybersecurity , Northwestern Polytechnical University , Xi'an , 710072 , P. R. China; School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an , 710072 , Shaanxi , P. R. China"},{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an , 710072 , Shaanxi , P. R. China"}],"abstract":"In this paper, we present a technique of definitely addressing the pairwise constraints in the semi-supervised clustering. Our method contributes to formulating the cannot-link relations and propagating them over the affinity graph flexibly. The pairwise constrained instances are provably guaranteed to be in the same or different connected components of the graph. Combined with the Laplacian rank constraint, the proposed model learns a Pairwise Constrained structured Optimal Graph (PCOG), from which the specified c clusters supporting the known pairwise constraints are directly obtained. An efficient algorithm invoked by the label propagation is designed to solve the formulation. Additionally, we also provide a compact criterion to acquire the key pairwise constraints for prompting the semi-supervised graph clustering. Substantial experimental results show that the proposed method achieves the significant improvements by using a few prior pairwise constraints.","doi":"10.24963/ijcai.2020/437","url":"https://ijcai.org/proceedings/2020/437","pdf":"https://ijcai.org/proceedings/2020/0437.pdf","markdown":"https://ijcai.org/proceedings/2020/437.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/437","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":437,"pages":{"first":3160,"last":3166},"keywords":["Machine Learning: Clustering","Machine Learning: Semi-Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24442/file_24442.m3u8","length_sec":279,"provider":"hls"}],"references":[{"position":1,"raw":"[Craenendonck et al., 2017] Toon Van Craenendonck, Se- bastijan Dumancic, and Hendrik Blockeel. 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