{"type":"ScholarlyArticle","title":"Learning with Adaptive Neighbors for Image Clustering","authors":[{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"},{"name":"Zhaohua Yang","slug":"zhaohua-yang","given":"Zhaohua","family":"Yang","affiliation":"Beihang University , Beijing , China"},{"name":"Shujian Wang","slug":"shujian-wang","given":"Shujian","family":"Wang","affiliation":"State Key Laboratory of Integrated Services Networks , Xidian University , Xi'an , China"}],"abstract":"Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.","doi":"10.24963/ijcai.2018/344","url":"https://ijcai.org/proceedings/2018/344","pdf":"https://ijcai.org/proceedings/2018/0344.pdf","markdown":"https://ijcai.org/proceedings/2018/344.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/344","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":344,"pages":{"first":2483,"last":2489},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Learning Graphical Models","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Asuncion and Newman, 2007] Arthur Asuncion and David Newman. 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