{"type":"ScholarlyArticle","title":"Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery","authors":[{"name":"Zhao Zhang","slug":"zhao-zhang","given":"Zhao","family":"Zhang","affiliation":"Key Laboratory of Knowledge Engineering with Big Data , Hefei University of Technology , China; School of Computer Science and Technology , Soochow University , China"},{"name":"Jiahuan Ren","slug":"jiahuan-ren","given":"Jiahuan","family":"Ren","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Zheng Zhang","slug":"zheng-zhang","given":"Zheng","family":"Zhang","affiliation":"Bio-Computing Research Center , Harbin Institute of Technology , Shenzhen , China"},{"name":"Guangcan Liu","slug":"guangcan-liu","given":"Guangcan","family":"Liu","affiliation":"School of Information and Control , Nanjing University of Information Science and Technology , China"}],"abstract":"Low-rank representation is powerful for recover-ing and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the sin-gle-layer latent low-rank models into multi-ple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and struc-tures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate fea-tures and subspaces for representation. To learn deep information, DLRF-Net inputs shallow fea-tures of the last layers into subsequent layers. Then, it recovers the deeper features and hierar-chical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical sub-spaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multi-layers using DLRF-Net. Extensive results show that our network can deliver enhanced perfor-mance over other related frameworks.","doi":"10.24963/ijcai.2020/383","url":"https://ijcai.org/proceedings/2020/383","pdf":"https://ijcai.org/proceedings/2020/0383.pdf","markdown":"https://ijcai.org/proceedings/2020/383.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/383","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":383,"pages":{"first":2762,"last":2768},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Deep Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24145/file_24145.m3u8","length_sec":277,"provider":"hls"}],"references":[{"position":1,"raw":"[Acharya et al., 2019] Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit S Dhillon. 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