{"type":"ScholarlyArticle","title":"Cascaded Low Rank and Sparse Representation on Grassmann Manifolds","authors":[{"name":"Boyue Wang","slug":"boyue-wang","given":"Boyue","family":"Wang","affiliation":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology , China; Faculty of Information Technology , Beijing University of Technology , China"},{"name":"Yongli Hu","slug":"yongli-hu","given":"Yongli","family":"Hu","affiliation":"Beijing Advanced Innovation Center for Future Internet Technology , China; Beijing Key Laboratory of Multimedia and Intelligent Software Technology , China; Faculty of Information Technology , Beijing University of Technology , China"},{"name":"Junbin Gao","slug":"junbin-gao","given":"Junbin","family":"Gao","affiliation":"The University of Sydney Business School , University of Sydney , NSW 2006 , Australia"},{"name":"Yanfeng Sun","slug":"yanfeng-sun","given":"Yanfeng","family":"Sun","affiliation":"Beijing Advanced Innovation Center for Future Internet Technology , China; Beijing Key Laboratory of Multimedia and Intelligent Software Technology , China; Faculty of Information Technology , Beijing University of Technology , China"},{"name":"Baocai Yin","slug":"baocai-yin","given":"Baocai","family":"Yin","affiliation":"Faculty of Electronic Information and Electrical Engineering , Dalian University of Technology , China; Beijing Advanced Innovation Center for Future Internet Technology , China; Beijing Key Laboratory of Multimedia and Intelligent Software Technology , China; Faculty of Information Technology , Beijing University of Technology , China"}],"abstract":"Inspired by low rank representation and sparse subspace clustering acquiring success, ones attempt to simultaneously perform low rank and sparse constraints on the affinity matrix to improve the performance. However, it is just a trade-off between these two constraints. In this paper, we propose a novel Cascaded Low Rank and Sparse Representation (CLRSR) method for subspace clustering, which seeks the sparse expression on the former learned low rank latent representation. To make our proposed method suitable to multi-dimension or imageset data, we extend CLRSR onto Grassmann manifolds. An effective solution and its convergence analysis are also provided. The excellent experimental results demonstrate the proposed method is more robust than other state-of-the-art clustering methods on imageset data.","doi":"10.24963/ijcai.2018/382","url":"https://ijcai.org/proceedings/2018/382","pdf":"https://ijcai.org/proceedings/2018/0382.pdf","markdown":"https://ijcai.org/proceedings/2018/382.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/382","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":382,"pages":{"first":2755,"last":2761},"keywords":["Machine Learning: Clustering","Computer Vision: Computer Vision","Machine Learning: Feature Selection ; Learning Sparse Models","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Absil et al., 2008] P. 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