{"type":"ScholarlyArticle","title":"Attributed Subspace Clustering","authors":[{"name":"Jing Wang","slug":"jing-wang","given":"Jing","family":"Wang","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo , Japan"},{"name":"Linchuan Xu","slug":"linchuan-xu","given":"Linchuan","family":"Xu","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo , Japan"},{"name":"Feng Tian","slug":"feng-tian","given":"Feng","family":"Tian","affiliation":"Faculty of Science and Technology , Bournemouth University , UK"},{"name":"Atsushi Suzuki","slug":"atsushi-suzuki","given":"Atsushi","family":"Suzuki","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo , Japan"},{"name":"Changqing Zhang","slug":"changqing-zhang","given":"Changqing","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Kenji Yamanishi","slug":"kenji-yamanishi","given":"Kenji","family":"Yamanishi","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo , Japan"}],"abstract":"Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders. Each attribute is distinct and complementary on depicting the data. Failing to explore attributes and capture the complementary information  among them may lead to an inaccurate representation. Moreover,  a single  clustering solution  is rather limited to depict data,  which can often be interpreted from different aspects and grouped into multiple clusters according to attributes. Therefore, we propose an innovative model called attributed subspace clustering (ASC). It  simultaneously learns multiple self-representations on latent representations derived from  original data. By utilizing Hilbert Schmidt Independence Criterion as a co-regularizing term, ASC enforces that each self-representation is independent and corresponds to a specific attribute. A more comprehensive self-representation is then established by adding these self-representations. Experiments on several benchmark image datasets have demonstrated  the effectiveness of  ASC not only in terms of clustering accuracy achieved by the integrated representation, but also the diverse interpretation of data, which is beyond what current approaches can offer.","doi":"10.24963/ijcai.2019/516","url":"https://ijcai.org/proceedings/2019/516","pdf":"https://ijcai.org/proceedings/2019/0516.pdf","markdown":"https://ijcai.org/proceedings/2019/516.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/516","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":516,"pages":{"first":3719,"last":3725},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Bartels and Stewart, 1972] Richard H. 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