{"type":"ScholarlyArticle","title":"Spectral Perturbation Meets Incomplete Multi-view Data","authors":[{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"School of Information Science and Technology , Southwest Jiaotong University , Chengdu , China; Department of Computer Science , University of Illinois at Chicago , Chicago , USA"},{"name":"Linlin Zong","slug":"linlin-zong","given":"Linlin","family":"Zong","affiliation":"School of Software , Dalian University of Technology , Dalian , China"},{"name":"Bing Liu","slug":"bing-liu","given":"Bing","family":"Liu","affiliation":"Department of Computer Science , University of Illinois at Chicago , Chicago , USA"},{"name":"Yan Yang","slug":"yan-yang","given":"Yan","family":"Yang","affiliation":"School of Information Science and Technology , Southwest Jiaotong University , Chengdu , China"},{"name":"Wei Zhou","slug":"wei-zhou","given":"Wei","family":"Zhou","affiliation":"School of Information Science and Technology , Southwest Jiaotong University , Chengdu , China"}],"abstract":"Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similarity matrix. Moreover, we show that the minimization of perturbation risk bounds among different views maximizes the final fusion result across all views. This provides a solid fusion criteria for multi-view data. We motivate and propose a Perturbation-oriented Incomplete multi-view Clustering (PIC) method. Experimental results demonstrate the effectiveness of the proposed method.","doi":"10.24963/ijcai.2019/510","url":"https://ijcai.org/proceedings/2019/510","pdf":"https://ijcai.org/proceedings/2019/0510.pdf","markdown":"https://ijcai.org/proceedings/2019/510.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/510","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":510,"pages":{"first":3677,"last":3683},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Cai and Chen, 2015] Deng Cai and Xinlei Chen. 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