{"type":"ScholarlyArticle","title":"Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data","authors":[{"name":"Yanyong Huang","slug":"yanyong-huang","given":"Yanyong","family":"Huang","affiliation":"Joint Laboratory of Data Science and Business Intelligence , School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Zongxin Shen","slug":"zongxin-shen","given":"Zongxin","family":"Shen","affiliation":"Joint Laboratory of Data Science and Business Intelligence , School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Tianrui Li","slug":"tianrui-li","given":"Tianrui","family":"Li","affiliation":"School of Computing and Artificial Intelligence , Southwest Jiaotong University , Chengdu , China"},{"name":"Fengmao Lv","slug":"fengmao-lv","given":"Fengmao","family":"Lv","affiliation":"School of Computing and Artificial Intelligence , Southwest Jiaotong University , Chengdu , China"}],"abstract":"Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined values to impute missing data, then perform feature selection on the complete dataset. Separating imputation and feature selection processes fails to capitalize on the potential synergy where local structural information gleaned from feature selection could guide the imputation, thereby improving the feature selection performance in turn. Additionally, previous methods only focus on leveraging samples' local structure information, while ignoring the intrinsic locality of the feature space. To tackle these problems, a novel MUFS method, called UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces. Then, UNIFIER dynamically recovers the missing views, guided by the sample and feature similarity graphs during the feature selection procedure. Furthermore, the half-quadratic minimization technique is used to automatically weight different instances, alleviating the impact of outliers and unreliable restored data. Comprehensive experimental results demonstrate that UNIFIER outperforms other state-of-the-art methods.","doi":"10.24963/ijcai.2024/463","url":"https://ijcai.org/proceedings/2024/463","pdf":"https://ijcai.org/proceedings/2024/0463.pdf","markdown":"https://ijcai.org/proceedings/2024/463.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/463","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":463,"pages":{"first":4192,"last":4200},"keywords":["Machine Learning: ML: Feature extraction, selection and dimensionality reduction","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Xiangpin Bai, Lei Zhu, Cheng Liang, Jingjing Li, Xiushan Nie, and Xiaojun Chang. 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