{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:44Z","timestamp":1758672884780,"version":"3.44.0"},"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":[[2025,9]]},"abstract":"<jats:p>Tensor Robust Principal Component Analysis (TRPCA) has emerged as a powerful technique for low-rank tensor recovery.  To achieve better recovery performance, a variety of TNN (Tensor Nuclear Norm) based low-rank regularizers have been proposed case by case, lacking  a general and flexible framework. In this paper, we design a novel tensor low-rank regularization framework coined  FGTNN (Flexible Generalized Tensor Nuclear Norm). Equipped with FGTNN,  we develop the FGTRPCA (Flexible Generalized TRPCA) framework, which has two desirable properties. 1) Generalizability: Many existing TRPCA methods can be viewed as special cases of our framework; 2) Flexibility: Using FGTRPCA as a general platform, we derive a series of new TRPCA methods by tuning a continuous parameter to improve performance. In addition, we develop another novel smooth and low-rank regularizer coined t-FGJP and the resulting SFGTRPCA (Smooth FGTRPCA) method by leveraging the low-rankness and smoothness priors simultaneously. Experimental results on various tensor denoising and recovery tasks demonstrate the superiority of our methods.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/583","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5235-5243","source":"Crossref","is-referenced-by-count":0,"title":["Flexible Generalized Low-Rank Regularizer for Tensor RPCA"],"prefix":"10.24963","author":[{"given":"Zhiyang","family":"Gong","sequence":"first","affiliation":[{"name":"Huazhong Agricultural University"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University"}]},{"given":"Yutao","family":"Hu","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University"}]},{"given":"Yulong","family":"Wang","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University"},{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, China"},{"name":"Key Laboratory of Smart Farming Technology for Agricultural Animals, Ministry of Agriculture and Rural Affairs, China"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:29Z","timestamp":1758627269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/583"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/583","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}