{"type":"ScholarlyArticle","title":"Flexible Multi-View Representation Learning for Subspace Clustering","authors":[{"name":"Ruihuang Li","slug":"ruihuang-li","given":"Ruihuang","family":"Li","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Changqing Zhang","slug":"changqing-zhang","given":"Changqing","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China; Jiangsu Key Laboratory of Big Data Security & Intelligent Processing , Nanjing University of Posts and Telecommunications , Nanjing , China"},{"name":"Qinghua Hu","slug":"qinghua-hu","given":"Qinghua","family":"Hu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Pengfei Zhu","slug":"pengfei-zhu","given":"Pengfei","family":"Zhu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"}],"abstract":"In recent years, numerous multi-view subspace clustering methods have been proposed to exploit the complementary information from multiple views. Most of them perform data reconstruction within each single view, which makes the subspace representation unpromising and thus can not well identify the underlying relationships among data. In this paper, we propose to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction. The latent representation is flexibly constructed  by enforcing it to be close to different views, which implicitly makes it more comprehensive and well-adapted to subspace clustering. With the introduction of kernel dependence measure, the latent representation can flexibly encode complementary information from different views and explore nonlinear, high-order correlations among these views. We employ the Alternating Direction Minimization (ADM) method to solve our problem. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.","doi":"10.24963/ijcai.2019/404","url":"https://ijcai.org/proceedings/2019/404","pdf":"https://ijcai.org/proceedings/2019/0404.pdf","markdown":"https://ijcai.org/proceedings/2019/404.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/404","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 A-L","track":"Main track","number":404,"pages":{"first":2916,"last":2922},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Amini et al., 2009] Massih-Reza Amini, Nicolas Usunier, and Cyril Goutte. 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