{"type":"ScholarlyArticle","title":"Multi-view Feature Learning with Discriminative Regularization","authors":[{"name":"Jinglin Xu","slug":"jinglin-xu","given":"Jinglin","family":"Xu","affiliation":"Northwestern Polytechnical University , Xi'an , 710072 , P. R. China"},{"name":"Junwei Han","slug":"junwei-han","given":"Junwei","family":"Han","affiliation":"Northwestern Polytechnical University , Xi'an , 710072 , P. R. China"},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"Northwestern Polytechnical University , Xi'an , 710072 , P. R. China"}],"abstract":"More and more multi-view data which can capture rich information from heterogeneous features are widely used in real world applications. How to integrate different types of features, and how to learn low dimensional and discriminative information from high dimensional data are two main challenges. To address these challenges, this paper proposes a novel multi-view feature learning framework, which is regularized by discriminative information and obtains a feature learning model that contains multiple discriminative feature weighting matrices for different views, and then yields multiple low dimensional features used for subsequent multi-view clustering. To optimize the formulated objective function, we transform the proposed framework into a trace optimization problem which obtains the global solution in a closed form. Experimental evaluations on four widely used datasets and comparisons with a number of state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.","doi":"10.24963/ijcai.2017/441","url":"https://ijcai.org/proceedings/2017/441","pdf":"https://ijcai.org/proceedings/2017/0441.pdf","markdown":"https://ijcai.org/proceedings/2017/441.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/441","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":441,"pages":{"first":3161,"last":3167},"keywords":["Machine Learning: Multi-instance/Multi-label/Multi-view learning","Machine Learning: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Arora and Livescu, 2014] Raman Arora and Karen Livescu. 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