{"type":"ScholarlyArticle","title":"Robust Feature Selection on Incomplete Data","authors":[{"name":"Wei Zheng","slug":"wei-zheng","given":"Wei","family":"Zheng","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , Guangxi Normal University , Guilin , 541004 , China"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , Guangxi Normal University , Guilin , 541004 , China"},{"name":"Yonghua Zhu","slug":"yonghua-zhu","given":"Yonghua","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , Guangxi Normal University , Guilin , 541004 , China"},{"name":"Shichao Zhang","slug":"shichao-zhang","given":"Shichao","family":"Zhang","affiliation":"Guangxi Key Lab of Multi-source Information Mining & Security , Guangxi Normal University , Guilin , 541004 , China"}],"abstract":"Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis,but previous feature selection methods usually ignore sample diversity (i.e., every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.","doi":"10.24963/ijcai.2018/443","url":"https://ijcai.org/proceedings/2018/443","pdf":"https://ijcai.org/proceedings/2018/0443.pdf","markdown":"https://ijcai.org/proceedings/2018/443.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/443","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":443,"pages":{"first":3191,"last":3197},"keywords":["Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Feature Selection ; Learning Sparse Models","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2009] Samy Bengio, Fernando Pereira, Yoram Singer, and Dennis Strelow. 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