{"type":"ScholarlyArticle","title":"Adaptive Hypergraph Learning for Unsupervised Feature Selection","authors":[{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , China; Guangxi Normal University , China"},{"name":"Yonghua Zhu","slug":"yonghua-zhu","given":"Yonghua","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , China; Guangxi University , China"},{"name":"Shichao Zhang","slug":"shichao-zhang","given":"Shichao","family":"Zhang","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , China; Guangxi Normal University , China"},{"name":"Rongyao Hu","slug":"rongyao-hu","given":"Rongyao","family":"Hu","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , China; Guangxi Normal University , China"},{"name":"Wei He","slug":"wei-he","given":"Wei","family":"He","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , China; Guangxi Normal University , China"}],"abstract":"Current unsupervised feature selection (UFS) methods learn the similarity matrix by using a simple graph which is learnt from the original data as well as is independent from the process of feature selection, and thus unable to efficiently remove the redundant/irrelevant features. To address these issues, we propose a new UFS method to jointly learn the similarity matrix and conduct both subspace learning (via learning a dynamic hypergraph) and feature selection (via a sparsity constraint). As a result, we reduce the feature dimensions using different methods (i.e., subspace learning and feature selection) from different feature spaces, and thus makes our method select the informative features effectively and robustly. We tested our method using benchmark datasets to conduct the clustering tasks using the selected features, and the experimental results show that our proposed method outperforms all the comparison methods.","doi":"10.24963/ijcai.2017/501","url":"https://ijcai.org/proceedings/2017/501","pdf":"https://ijcai.org/proceedings/2017/0501.pdf","markdown":"https://ijcai.org/proceedings/2017/501.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/501","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 S-Z","track":"Main track","number":501,"pages":{"first":3581,"last":3587},"keywords":["Machine Learning: Feature Selection/Construction","Machine Learning: Machine Learning","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Ang et al., 2016] Jun Chin Ang, Andri Mirzal, Habibollah Haron, and Haza Nuzly Abdull Hamed. 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