{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:27:27Z","timestamp":1760956047399},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/383","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"2762-2768","source":"Crossref","is-referenced-by-count":12,"title":["Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery"],"prefix":"10.24963","author":[{"given":"Zhao","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, China"},{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahuan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangcan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Control, Nanjing University of Information Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:14:49Z","timestamp":1594260889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/383"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/383","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}