{"type":"ScholarlyArticle","title":"Latent Distribution Preserving Deep Subspace Clustering","authors":[{"name":"Lei Zhou","slug":"lei-zhou","given":"Lei","family":"Zhou","affiliation":"School of Computer Science and Engineering , Beijing Advanced Innovation Center for Big Data and Brain Computing , Jiangxi Research Institute , Beihang University , Beijing , China"},{"name":"Xiao Bai","slug":"xiao-bai","given":"Xiao","family":"Bai","affiliation":"School of Computer Science and Engineering , Beijing Advanced Innovation Center for Big Data and Brain Computing , Jiangxi Research Institute , Beihang University , Beijing , China"},{"name":"Dong Wang","slug":"dong-wang","given":"Dong","family":"Wang","affiliation":"School of Computer Science and Engineering , Beijing Advanced Innovation Center for Big Data and Brain Computing , Jiangxi Research Institute , Beihang University , Beijing , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"State Key Laboratory of Software Development Environment , Beihang University , Beijing , China; School of Computer Science and Engineering , Beijing Advanced Innovation Center for Big Data and Brain Computing , Jiangxi Research Institute , Beihang University , Beijing , China"},{"name":"Jun Zhou","slug":"jun-zhou","given":"Jun","family":"Zhou","affiliation":"School of Information and Communication Technology , Griffith University , Nathan , Australia"},{"name":"Edwin Hancock","slug":"edwin-hancock","given":"Edwin","family":"Hancock","affiliation":"Department of Computer Science , University of York , York , U.K.; School of Computer Science and Engineering , Beijing Advanced Innovation Center for Big Data and Brain Computing , Jiangxi Research Institute , Beihang University , Beijing , China"}],"abstract":"Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based subspace clustering methods have achieved success owning to the more powerful representation extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering. Experimental results on several public databases show that our method achieves significant improvement compared with the state-of-the-art subspace clustering methods.","doi":"10.24963/ijcai.2019/617","url":"https://ijcai.org/proceedings/2019/617","pdf":"https://ijcai.org/proceedings/2019/0617.pdf","markdown":"https://ijcai.org/proceedings/2019/617.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/617","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":617,"pages":{"first":4440,"last":4446},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Agarwal and Mustafa, 2004] Pankaj K Agarwal and N- abil H Mustafa. 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