文献类型：期刊文献

英文题名：Scalable one-stage multi-view subspace clustering with dictionary learning

作者：Guo, Wei[1,2];Wang, Zhe[1,2];Chi, Ziqiu[1,2];Xu, Xinlei[1,2];Li, Dongdong[2];Wu, Songyang[3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Minist Publ Secur, Key Lab Informat Network Secur, Shanghai 201204, Peoples R China

年份：2023

卷号：259

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20224713149073);WOS:【SCI-EXPANDED(收录号:WOS:000935578900009)】；

基金：This work is supported by Shanghai Science and Technology Program ''Federated based cross -domain and cross -task incremental learning'' under Grant No. 21511100800, Natural Science Foundation of China under Grant No. 62076094, Shanghai Science and Technology Program ''Distributed and generative few -shot algorithm and theory research'' under Grant No. 20511100600, Chinese Defense Program of Science and Technology under Grant No. 2021-JCJQ-JJ-0041, China Aerospace Science and Technology Corporation Industry -University -Research Cooperation Foundation of the Eighth Research Institute under Grant No. SAST2021- 007, Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) under Grant No. C20603.

语种：英文

外文关键词：Multi -view clustering; Large-scale datasets; Matrix factorization; Dictionary learning; Subspace learning

摘要：The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.