Deep self-weighted multi-view fuzzy clustering
Multi-view clustering has attracted considerable attention in various fields, such as computer vision and information retrieval. Most existing methods adopt a stepwise strategy to achieve a consistent representation and produce final clusters. However, this strategy neglects label consistency for th...
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| Published in: | Knowledge-based systems Vol. 328; p. 114158 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
25.10.2025
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| ISSN: | 0950-7051 |
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| Abstract | Multi-view clustering has attracted considerable attention in various fields, such as computer vision and information retrieval. Most existing methods adopt a stepwise strategy to achieve a consistent representation and produce final clusters. However, this strategy neglects label consistency for the same sample across different views, which results in sub-optimal representations. Furthermore, conventional methods frequently overlook the potential fuzzy membership relationships inherent in multi-view data and predominantly rely on shallow models, which fail to capture the complex properties of data, resulting in unsatisfactory outcomes. To address these challenges, we propose a novel deep self-weighted multi-view fuzzy clustering method that thoroughly explores the intricate view-specific characteristics of data to better represent consensus membership (i.e. consistent representation) between samples and centroids across multiple views. In particular, the method uses deep auto-encoders to non-linearly project samples from each view into corresponding latent spaces in a layer-wise manner. The consensus membership is then shared by samples from the middle and reconstruction layers, thereby reducing discrepancies in soft cluster assignment between the same sample in the latent and original spaces. Without introducing additional parameters, the self-weighted strategy adjusts the contribution of each view to fuzzy clustering. In addition, we adopt entropy regularization to tune the uniformity of the membership and design an alternating optimization algorithm to update all variables. Experimental results demonstrate the superior performance of the proposed method on five datasets (including images, web pages and videos) evaluated using four metrics. |
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| AbstractList | Multi-view clustering has attracted considerable attention in various fields, such as computer vision and information retrieval. Most existing methods adopt a stepwise strategy to achieve a consistent representation and produce final clusters. However, this strategy neglects label consistency for the same sample across different views, which results in sub-optimal representations. Furthermore, conventional methods frequently overlook the potential fuzzy membership relationships inherent in multi-view data and predominantly rely on shallow models, which fail to capture the complex properties of data, resulting in unsatisfactory outcomes. To address these challenges, we propose a novel deep self-weighted multi-view fuzzy clustering method that thoroughly explores the intricate view-specific characteristics of data to better represent consensus membership (i.e. consistent representation) between samples and centroids across multiple views. In particular, the method uses deep auto-encoders to non-linearly project samples from each view into corresponding latent spaces in a layer-wise manner. The consensus membership is then shared by samples from the middle and reconstruction layers, thereby reducing discrepancies in soft cluster assignment between the same sample in the latent and original spaces. Without introducing additional parameters, the self-weighted strategy adjusts the contribution of each view to fuzzy clustering. In addition, we adopt entropy regularization to tune the uniformity of the membership and design an alternating optimization algorithm to update all variables. Experimental results demonstrate the superior performance of the proposed method on five datasets (including images, web pages and videos) evaluated using four metrics. |
| ArticleNumber | 114158 |
| Author | Shi, Mei Zhao, Xiaowei Xiao, Yun Guo, Jun Yin, Xiaoyan |
| Author_xml | – sequence: 1 givenname: Mei surname: Shi fullname: Shi, Mei organization: Guangdong Provincial Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, 518060, China – sequence: 2 givenname: Xiaowei surname: Zhao fullname: Zhao, Xiaowei organization: State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment, The Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, Shaanxi, China – sequence: 3 givenname: Xiaoyan orcidid: 0000-0003-3265-2954 surname: Yin fullname: Yin, Xiaoyan organization: School of Information Science and Technology, Northwest University, Xi’an, 710127, China – sequence: 4 givenname: Yun surname: Xiao fullname: Xiao, Yun organization: School of Information Science and Technology, Northwest University, Xi’an, 710127, China – sequence: 5 givenname: Jun surname: Guo fullname: Guo, Jun email: guojun@nwu.edu.cn organization: School of Information Science and Technology, Northwest University, Xi’an, 710127, China |
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| Cites_doi | 10.1109/TKDE.2019.2903810 10.1016/j.eswa.2016.10.006 10.1016/j.knosys.2021.107751 10.1016/j.neucom.2021.03.090 10.1109/TKDE.2015.2448542 10.1016/j.knosys.2018.10.022 10.1109/TKDE.2021.3068461 10.1016/j.procs.2022.01.009 10.1016/j.knosys.2021.106807 10.1142/S012906570000034X 10.1016/j.neucom.2021.06.011 10.1016/j.neucom.2019.12.054 10.1016/j.neucom.2020.11.074 10.1016/j.patcog.2021.108064 10.1109/TFUZZ.2023.3335361 10.1016/j.knosys.2020.106273 10.1016/j.inffus.2018.11.019 10.1109/TPAMI.2018.2877660 10.1016/j.patcog.2023.109836 10.1016/j.patcog.2022.108815 10.1007/s13042-021-01307-7 10.1109/TSP.2019.2910475 10.1016/j.neucom.2019.06.098 10.1109/TCYB.2014.2334595 10.1016/j.neucom.2020.02.104 10.1109/TIP.2021.3083072 10.1016/j.knosys.2019.105102 10.1016/j.neucom.2019.11.070 10.1109/TMM.2020.3025666 10.1016/j.knosys.2023.110424 10.1109/TFUZZ.2024.3389705 10.1109/TIP.2017.2754939 10.1016/0098-3004(84)90020-7 10.1016/j.ins.2021.11.075 10.1109/TMM.2009.2030629 10.26599/BDMA.2018.9020003 |
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| Keywords | Deep learning Fuzzy clustering Alternating optimization algorithm Information entropy Multi-view learning |
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| References | Cleuziou, Exbrayat, Martin, Sublemontier (bib0028) 2009 Zhang, Xu, Lu, Huang (bib0053) 2009; 11 Luong, Nayak, Balasubramaniam, Bashar (bib0046) 2022; 131 Li, Zhang, Fu, Peng, Zhou, Hu (bib0047) 2019 Chen, Rouditchenko, Duarte, Kuehne, Thomas, Boggust, Panda, Kingsbury, Feris, Harwath, Glass, Picheny, Chang (bib0006) 2021 Pehlivan, Turksen (bib0026) 2021; 24 Wang, Yang, Liu, Fujita (bib0040) 2019; 163 Reformat, Chen, Yager (bib0025) 2018; volume 855 Huang, Zhou, Zhu, Zhang, Lv, Peng (bib0036) 2021; 30 Yang, Wang (bib0004) 2018; 1 Fu, Lin, Vasilakos, Wang (bib0003) 2020; 402 Zhang, Hu, Fu, Zhu, Cao (bib0023) 2017 Chang, Xiang, Hospedales (bib0042) 2018 Nie, Cai, Li, Li (bib0049) 2017; 27 Zhang, Nie, Li, Wei (bib0001) 2019; 50 Rupnik, Shawe-Taylor (bib0012) 2010; volume 473 A. Asuncion, D. Newman, Uci machine learning repository, 2007. Hu, Qin, Shen, Pedrycz, Liu, Liu (bib0055) 2024; 32 Kang, Shi, Huang, Chen, Pu, Zhou, Xu (bib0020) 2020; 189 Zhao, Yang, Nie (bib0058) 2023; 144 Borlea, Precup, Borlea (bib0027) 2022; 199 Nie, Li, Li (bib0056) 2017 Huang, Tsang, Xu, Lv (bib0057) 2022; 34 Wang, Arora, Livescu, Bilmes (bib0035) 2015 Wang, Xiao, Zhu, Guo (bib0039) 2022; 586 Zhao, Ding, Fu (bib0043) 2017 Shi, Lu, Fang, Zhang (bib0048) 2024; 32 Zhao, Li, Ahmed (bib0007) 2020; 206 Chen, Qian, Chen, Zheng, Zhu (bib0021) 2019; 366 Bezdek, Ehrlich, Full (bib0054) 1984; 10 Wang, Chen, Li (bib0032) 2017 Zhang, Fu, Hu, Cao, Xie, Tao, Xu (bib0024) 2018; 42 Jiang, Chung, Wang, Deng, Wang, Qian (bib0029) 2015; 45 Liu, Wang, Gao, Han (bib0015) 2013 Wang, Chen (bib0031) 2017; 72 Cai, Nie, Cai, Huang (bib0050) 2013 Zou, Tang, Zheng, Sun, Zhang, Ding (bib0041) 2023; 267 Lai, Fyfe (bib0014) 2000; 10 Zhang, Zhang, Li, Xu (bib0038) 2021; 458 Wang, Cheng, Gao, Zhao, Jiao (bib0037) 2020; 23 Khan, Hu, Li, Diallo, Wang (bib0019) 2022; 13 Sanghavi, Verma (bib0010) 2022 Wang, Wang, Tian, Liu, Yu, Liu (bib0016) 2016 Härdle, Simar (bib0011) 2015 Guo, Shi, Zhu, Huang, He, Zhang, Tang (bib0002) 2021; 458 Li, Zhou, Qiu, Wang, Zhang, Xie (bib0044) 2020; 390 Chen, Wang, Giannakis (bib0013) 2019; 67 Yang, Sinaga (bib0030) 2021; 119 Wu, Lin, Han, Liu, Ding, Zhang, Shen (bib0005) 2018 Chang, Hu, Li, Wang, Peng (bib0045) 2021; 217 Huang, Xu, Kang, Ren (bib0017) 2019; 382 Yan, Hu, Mao, Ye, Yu (bib0033) 2021; 448 Guan, Zhang, Peng, Fan (bib0009) 2015; 27 Ma, Wang, Lin, Pan, Zhang, Yang (bib0008) 2022; 236 Fei-Fei, Fergus, Perona (bib0051) 2004 Kapil, Chawla (bib0059) 2016 Liu, Wang, Lu, Luo (bib0018) 2020 Wang, Yang, Liu (bib0022) 2019; 32 Andrew, Arora, Bilmes, Livescu (bib0034) 2013 Guo (10.1016/j.knosys.2025.114158_bib0002) 2021; 458 Huang (10.1016/j.knosys.2025.114158_bib0057) 2022; 34 Liu (10.1016/j.knosys.2025.114158_bib0015) 2013 Jiang (10.1016/j.knosys.2025.114158_bib0029) 2015; 45 Chang (10.1016/j.knosys.2025.114158_bib0045) 2021; 217 Zhang (10.1016/j.knosys.2025.114158_bib0001) 2019; 50 Borlea (10.1016/j.knosys.2025.114158_bib0027) 2022; 199 10.1016/j.knosys.2025.114158_bib0052 Yang (10.1016/j.knosys.2025.114158_bib0004) 2018; 1 Cai (10.1016/j.knosys.2025.114158_bib0050) 2013 Li (10.1016/j.knosys.2025.114158_bib0044) 2020; 390 Huang (10.1016/j.knosys.2025.114158_bib0017) 2019; 382 Kang (10.1016/j.knosys.2025.114158_bib0020) 2020; 189 Lai (10.1016/j.knosys.2025.114158_bib0014) 2000; 10 Chen (10.1016/j.knosys.2025.114158_bib0013) 2019; 67 Reformat (10.1016/j.knosys.2025.114158_bib0025) 2018; volume 855 Cleuziou (10.1016/j.knosys.2025.114158_bib0028) 2009 Shi (10.1016/j.knosys.2025.114158_bib0048) 2024; 32 Wang (10.1016/j.knosys.2025.114158_bib0016) 2016 Hu (10.1016/j.knosys.2025.114158_bib0055) 2024; 32 Zou (10.1016/j.knosys.2025.114158_bib0041) 2023; 267 Huang (10.1016/j.knosys.2025.114158_bib0036) 2021; 30 Andrew (10.1016/j.knosys.2025.114158_bib0034) 2013 Wang (10.1016/j.knosys.2025.114158_bib0031) 2017; 72 Wang (10.1016/j.knosys.2025.114158_bib0040) 2019; 163 Fei-Fei (10.1016/j.knosys.2025.114158_bib0051) 2004 Guan (10.1016/j.knosys.2025.114158_bib0009) 2015; 27 Wang (10.1016/j.knosys.2025.114158_bib0035) 2015 Zhang (10.1016/j.knosys.2025.114158_bib0053) 2009; 11 Khan (10.1016/j.knosys.2025.114158_bib0019) 2022; 13 Wu (10.1016/j.knosys.2025.114158_bib0005) 2018 Li (10.1016/j.knosys.2025.114158_bib0047) 2019 Yan (10.1016/j.knosys.2025.114158_bib0033) 2021; 448 Wang (10.1016/j.knosys.2025.114158_bib0032) 2017 Yang (10.1016/j.knosys.2025.114158_bib0030) 2021; 119 Fu (10.1016/j.knosys.2025.114158_bib0003) 2020; 402 Chang (10.1016/j.knosys.2025.114158_bib0042) 2018 Nie (10.1016/j.knosys.2025.114158_bib0049) 2017; 27 Zhao (10.1016/j.knosys.2025.114158_bib0058) 2023; 144 Zhang (10.1016/j.knosys.2025.114158_bib0023) 2017 Sanghavi (10.1016/j.knosys.2025.114158_bib0010) 2022 Nie (10.1016/j.knosys.2025.114158_bib0056) 2017 Ma (10.1016/j.knosys.2025.114158_bib0008) 2022; 236 Wang (10.1016/j.knosys.2025.114158_bib0022) 2019; 32 Wang (10.1016/j.knosys.2025.114158_bib0037) 2020; 23 Luong (10.1016/j.knosys.2025.114158_bib0046) 2022; 131 Wang (10.1016/j.knosys.2025.114158_bib0039) 2022; 586 Pehlivan (10.1016/j.knosys.2025.114158_bib0026) 2021; 24 Härdle (10.1016/j.knosys.2025.114158_bib0011) 2015 Kapil (10.1016/j.knosys.2025.114158_bib0059) 2016 Bezdek (10.1016/j.knosys.2025.114158_bib0054) 1984; 10 Zhao (10.1016/j.knosys.2025.114158_bib0007) 2020; 206 Chen (10.1016/j.knosys.2025.114158_bib0006) 2021 Chen (10.1016/j.knosys.2025.114158_bib0021) 2019; 366 Zhang (10.1016/j.knosys.2025.114158_bib0038) 2021; 458 Rupnik (10.1016/j.knosys.2025.114158_bib0012) 2010; volume 473 Zhang (10.1016/j.knosys.2025.114158_bib0024) 2018; 42 Liu (10.1016/j.knosys.2025.114158_bib0018) 2020 Zhao (10.1016/j.knosys.2025.114158_bib0043) 2017 |
| References_xml | – volume: 119 year: 2021 ident: bib0030 article-title: Collaborative feature-weighted multi-view fuzzy c-means clustering publication-title: Pattern Recogn. – start-page: 1488 year: 2018 end-page: 1497 ident: bib0042 article-title: Scalable and effective deep CCA via soft decorrelation publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 4701 year: 2022 end-page: 4710 ident: bib0010 article-title: Multi-view multi-label canonical correlation analysis for cross-modal matching and retrieval publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops – volume: 10 start-page: 191 year: 1984 end-page: 203 ident: bib0054 article-title: Fcm: the fuzzy c-means clustering algorithm publication-title: Comput. Geosci. – volume: 32 start-page: 4038 year: 2024 end-page: 4048 ident: bib0048 article-title: Unsupervised domain adaptation enhanced by fuzzy prompt learning publication-title: IEEE Trans. Fuzzy Syst. – volume: 390 start-page: 108 year: 2020 end-page: 116 ident: bib0044 article-title: Deep graph regularized non-negative matrix factorization for multi-view clustering publication-title: Neurocomputing – volume: 382 start-page: 196 year: 2019 end-page: 209 ident: bib0017 article-title: Regularized nonnegative matrix factorization with adaptive local structure learning publication-title: Neurocomputing – volume: 32 start-page: 1886 year: 2024 end-page: 1899 ident: bib0055 article-title: An efficient federated multiview fuzzy c-means clustering method publication-title: IEEE Trans. Fuzzy Syst. – volume: 34 start-page: 5869 year: 2022 end-page: 5883 ident: bib0057 article-title: Measuring diversity in graph learning: a unified framework for structured multi-view clustering publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 4279 year: 2017 end-page: 4287 ident: bib0023 article-title: Latent multi-view subspace clustering publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 458 start-page: 14 year: 2021 end-page: 23 ident: bib0002 article-title: Improving human action recognition by jointly exploiting video and wifi clues publication-title: Neurocomputing – volume: 67 start-page: 2826 year: 2019 end-page: 2838 ident: bib0013 article-title: Graph multiview canonical correlation analysis publication-title: IEEE Trans. Signal Process. – volume: 402 start-page: 148 year: 2020 end-page: 161 ident: bib0003 article-title: An overview of recent multi-view clustering publication-title: Neurocomputing – volume: 206 year: 2020 ident: bib0007 article-title: Spidernet: a spiderweb graph neural network for multi-view gait recognition publication-title: Knowl. Based Syst. – start-page: 2564 year: 2017 end-page: 2570 ident: bib0056 article-title: Self-weighted multiview clustering with multiple graphs publication-title: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017 – start-page: 2971 year: 2017 end-page: 2977 ident: bib0032 article-title: Multiple medoids based multi-view relational fuzzy clustering with minimax optimization publication-title: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence – volume: 50 start-page: 158 year: 2019 end-page: 167 ident: bib0001 article-title: Feature selection with multi-view data: a survey publication-title: Inform. Fus. – start-page: 1083 year: 2015 end-page: 1092 ident: bib0035 article-title: On deep multi-view representation learning publication-title: International Conference on Machine Learning – volume: 144 year: 2023 ident: bib0058 article-title: Deep multi-view spectral clustering via ensemble publication-title: Pattern Recogn. – start-page: 1737 year: 2013 end-page: 1744 ident: bib0050 article-title: Heterogeneous image features integration via multi-modal semi-supervised learning model publication-title: Proceedings of the IEEE International Conference on Computer Vision – start-page: 435 year: 2016 end-page: 444 ident: bib0016 article-title: Adaptive multi-view semi-supervised nonnegative matrix factorization publication-title: International Conference on Neural Information Processing – volume: 199 start-page: 63 year: 2022 end-page: 70 ident: bib0027 article-title: Improvement of k-means cluster quality by post processing resulted clusters publication-title: Proc. Comput. Sci. – volume: 366 start-page: 1 year: 2019 end-page: 11 ident: bib0021 article-title: Auto-weighted multi-view constrained spectral clustering publication-title: Neurocomputing – volume: 30 start-page: 5352 year: 2021 end-page: 5362 ident: bib0036 article-title: Deep spectral representation learning from multi-view data publication-title: IEEE Trans. Image Process. – volume: 45 start-page: 688 year: 2015 end-page: 701 ident: bib0029 article-title: Collaborative fuzzy clustering from multiple weighted views publication-title: IEEE Trans. Cybern. – volume: 131 year: 2022 ident: bib0046 article-title: Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering publication-title: Pattern Recogn. – volume: 163 start-page: 1009 year: 2019 end-page: 1019 ident: bib0040 article-title: A study of graph-based system for multi-view clustering publication-title: Knowl. Based Syst. – volume: 586 start-page: 224 year: 2022 end-page: 238 ident: bib0039 article-title: Multi-view fuzzy clustering of deep random walk and sparse low-rank embedding publication-title: Inform. Sci. – volume: 11 start-page: 1276 year: 2009 end-page: 1288 ident: bib0053 article-title: Character identification in feature-length films using global face-name matching publication-title: IEEE Trans. Multimedia – volume: 72 start-page: 457 year: 2017 end-page: 466 ident: bib0031 article-title: Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources publication-title: Expert Syst. Appl. – volume: 24 start-page: 79 year: 2021 end-page: 98 ident: bib0026 article-title: A novel multiplicative fuzzy regression function with a multiplicative fuzzy clustering algorithm publication-title: Roman. J. Inform. Sci. Technol. – reference: A. Asuncion, D. Newman, Uci machine learning repository, 2007. – volume: 32 start-page: 1116 year: 2019 end-page: 1129 ident: bib0022 article-title: Gmc: graph-based multi-view clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 267 year: 2023 ident: bib0041 article-title: Inclusivity induced adaptive graph learning for multi-view clustering publication-title: Knowl. Based Syst. – year: 2004 ident: bib0051 article-title: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories publication-title: 2004 Conference on Computer Vision and Pattern Recognition Workshop – start-page: 1 year: 2016 end-page: 4 ident: bib0059 article-title: Performance evaluation of k-means clustering algorithm with various distance metrics publication-title: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) – start-page: 2921 year: 2017 end-page: 2927 ident: bib0043 article-title: Multi-view clustering via deep matrix factorization publication-title: Thirty-first AAAI Conference on Artificial Intelligence – start-page: 1247 year: 2013 end-page: 1255 ident: bib0034 article-title: Deep canonical correlation analysis publication-title: International Conference on Machine Learning – start-page: 752 year: 2009 end-page: 757 ident: bib0028 article-title: CoFKM: a centralized method for multiple-view clustering publication-title: ICDM 2009, The Ninth IEEE International Conference on Data Mining, Miami, Florida, USA, 6-9 December 2009 – volume: 42 start-page: 86 year: 2018 end-page: 99 ident: bib0024 article-title: Generalized latent multi-view subspace clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 236 year: 2022 ident: bib0008 article-title: A multi-view network for real-time emotion recognition in conversations publication-title: Knowl. Based Syst. – volume: 23 start-page: 3483 year: 2020 end-page: 3493 ident: bib0037 article-title: Deep multi-view subspace clustering with unified and discriminative learning publication-title: IEEE Trans. Multimedia – volume: 10 start-page: 365 year: 2000 end-page: 377 ident: bib0014 article-title: Kernel and nonlinear canonical correlation analysis publication-title: Int. J. Neural Syst. – start-page: 3964 year: 2020 end-page: 3971 ident: bib0018 article-title: Multi-view non-negative matrix factorization discriminant learning via cross entropy loss publication-title: 2020 Chinese Control And Decision Conference (CCDC) – start-page: 252 year: 2013 end-page: 260 ident: bib0015 article-title: Multi-view clustering via joint nonnegative matrix factorization publication-title: Proceedings of the 2013 SIAM International Conference on Data Mining – start-page: 8012 year: 2021 end-page: 8021 ident: bib0006 article-title: Multimodal clustering networks for self-supervised learning from unlabeled videos publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) – start-page: 2854 year: 2018 end-page: 2860 ident: bib0005 article-title: Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval publication-title: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence – volume: 448 start-page: 106 year: 2021 end-page: 129 ident: bib0033 article-title: Deep multi-view learning methods: a review publication-title: Neurocomputing – volume: 13 start-page: 677 year: 2022 end-page: 689 ident: bib0019 article-title: Multi-view data clustering via non-negative matrix factorization with manifold regularization publication-title: Int. J. Mach. Learn. Cybern. – volume: 458 start-page: 47 year: 2021 end-page: 55 ident: bib0038 article-title: Robust multi-view fuzzy clustering via softmin publication-title: Neurocomputing – volume: volume 473 start-page: 1 year: 2010 end-page: 4 ident: bib0012 article-title: Multi-view canonical correlation analysis publication-title: Conference on Data Mining and Data Warehouses (SiKDD 2010) – volume: volume 855 start-page: 715 year: 2018 end-page: 726 ident: bib0025 article-title: Clustering of propositions equipped with uncertainty publication-title: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part III – start-page: 8172 year: 2019 end-page: 8180 ident: bib0047 article-title: Reciprocal multi-layer subspace learning for multi-view clustering publication-title: Proceedings of the IEEE/CVF International Conference on Computer Vision – volume: 217 year: 2021 ident: bib0045 article-title: Multi-view clustering via deep concept factorization publication-title: Knowl. Based Syst. – start-page: 443 year: 2015 end-page: 454 ident: bib0011 article-title: Canonical correlation analysis publication-title: Applied Multivariate Statistical Analysis – volume: 27 start-page: 3016 year: 2015 end-page: 3028 ident: bib0009 article-title: Multi-view concept learning for data representation publication-title: IEEE Trans. Knowl. Data Eng. – volume: 27 start-page: 1501 year: 2017 end-page: 1511 ident: bib0049 article-title: Auto-weighted multi-view learning for image clustering and semi-supervised classification publication-title: IEEE Trans. Image Process. – volume: 1 start-page: 83 year: 2018 end-page: 107 ident: bib0004 article-title: Multi-view clustering: a survey publication-title: Big Data Mining Anal. – volume: 189 year: 2020 ident: bib0020 article-title: Multi-graph fusion for multi-view spectral clustering publication-title: Knowl. Based Syst. – volume: 32 start-page: 1116 issue: 6 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0022 article-title: Gmc: graph-based multi-view clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2903810 – volume: 72 start-page: 457 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0031 article-title: Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.10.006 – volume: 236 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0008 article-title: A multi-view network for real-time emotion recognition in conversations publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2021.107751 – start-page: 2854 year: 2018 ident: 10.1016/j.knosys.2025.114158_bib0005 article-title: Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval – volume: 448 start-page: 106 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0033 article-title: Deep multi-view learning methods: a review publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.03.090 – volume: 27 start-page: 3016 issue: 11 year: 2015 ident: 10.1016/j.knosys.2025.114158_bib0009 article-title: Multi-view concept learning for data representation publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2015.2448542 – volume: 163 start-page: 1009 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0040 article-title: A study of graph-based system for multi-view clustering publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2018.10.022 – volume: 34 start-page: 5869 issue: 12 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0057 article-title: Measuring diversity in graph learning: a unified framework for structured multi-view clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2021.3068461 – volume: 199 start-page: 63 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0027 article-title: Improvement of k-means cluster quality by post processing resulted clusters publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2022.01.009 – volume: 217 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0045 article-title: Multi-view clustering via deep concept factorization publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2021.106807 – volume: 10 start-page: 365 issue: 05 year: 2000 ident: 10.1016/j.knosys.2025.114158_bib0014 article-title: Kernel and nonlinear canonical correlation analysis publication-title: Int. J. Neural Syst. doi: 10.1142/S012906570000034X – volume: 458 start-page: 47 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0038 article-title: Robust multi-view fuzzy clustering via softmin publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.06.011 – year: 2004 ident: 10.1016/j.knosys.2025.114158_bib0051 article-title: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories – volume: 390 start-page: 108 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0044 article-title: Deep graph regularized non-negative matrix factorization for multi-view clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.12.054 – volume: volume 473 start-page: 1 year: 2010 ident: 10.1016/j.knosys.2025.114158_bib0012 article-title: Multi-view canonical correlation analysis – volume: 458 start-page: 14 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0002 article-title: Improving human action recognition by jointly exploiting video and wifi clues publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.11.074 – start-page: 1488 year: 2018 ident: 10.1016/j.knosys.2025.114158_bib0042 article-title: Scalable and effective deep CCA via soft decorrelation – start-page: 252 year: 2013 ident: 10.1016/j.knosys.2025.114158_bib0015 article-title: Multi-view clustering via joint nonnegative matrix factorization – volume: 119 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0030 article-title: Collaborative feature-weighted multi-view fuzzy c-means clustering publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2021.108064 – volume: 32 start-page: 1886 issue: 4 year: 2024 ident: 10.1016/j.knosys.2025.114158_bib0055 article-title: An efficient federated multiview fuzzy c-means clustering method publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2023.3335361 – start-page: 443 year: 2015 ident: 10.1016/j.knosys.2025.114158_bib0011 article-title: Canonical correlation analysis – start-page: 435 year: 2016 ident: 10.1016/j.knosys.2025.114158_bib0016 article-title: Adaptive multi-view semi-supervised nonnegative matrix factorization – start-page: 1247 year: 2013 ident: 10.1016/j.knosys.2025.114158_bib0034 article-title: Deep canonical correlation analysis – volume: 206 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0007 article-title: Spidernet: a spiderweb graph neural network for multi-view gait recognition publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2020.106273 – volume: 50 start-page: 158 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0001 article-title: Feature selection with multi-view data: a survey publication-title: Inform. Fus. doi: 10.1016/j.inffus.2018.11.019 – volume: 42 start-page: 86 issue: 1 year: 2018 ident: 10.1016/j.knosys.2025.114158_bib0024 article-title: Generalized latent multi-view subspace clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2877660 – volume: 144 year: 2023 ident: 10.1016/j.knosys.2025.114158_bib0058 article-title: Deep multi-view spectral clustering via ensemble publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2023.109836 – start-page: 2921 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0043 article-title: Multi-view clustering via deep matrix factorization – volume: 131 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0046 article-title: Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2022.108815 – volume: 13 start-page: 677 issue: 3 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0019 article-title: Multi-view data clustering via non-negative matrix factorization with manifold regularization publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-021-01307-7 – start-page: 752 year: 2009 ident: 10.1016/j.knosys.2025.114158_bib0028 article-title: CoFKM: a centralized method for multiple-view clustering – ident: 10.1016/j.knosys.2025.114158_bib0052 – start-page: 1 year: 2016 ident: 10.1016/j.knosys.2025.114158_bib0059 article-title: Performance evaluation of k-means clustering algorithm with various distance metrics – volume: 67 start-page: 2826 issue: 11 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0013 article-title: Graph multiview canonical correlation analysis publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2019.2910475 – volume: 366 start-page: 1 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0021 article-title: Auto-weighted multi-view constrained spectral clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.06.098 – volume: 45 start-page: 688 issue: 4 year: 2015 ident: 10.1016/j.knosys.2025.114158_bib0029 article-title: Collaborative fuzzy clustering from multiple weighted views publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2014.2334595 – volume: 402 start-page: 148 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0003 article-title: An overview of recent multi-view clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.02.104 – volume: 24 start-page: 79 issue: 1 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0026 article-title: A novel multiplicative fuzzy regression function with a multiplicative fuzzy clustering algorithm publication-title: Roman. J. Inform. Sci. Technol. – volume: 30 start-page: 5352 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0036 article-title: Deep spectral representation learning from multi-view data publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3083072 – start-page: 1737 year: 2013 ident: 10.1016/j.knosys.2025.114158_bib0050 article-title: Heterogeneous image features integration via multi-modal semi-supervised learning model – volume: 189 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0020 article-title: Multi-graph fusion for multi-view spectral clustering publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2019.105102 – volume: 382 start-page: 196 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0017 article-title: Regularized nonnegative matrix factorization with adaptive local structure learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.11.070 – volume: 23 start-page: 3483 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0037 article-title: Deep multi-view subspace clustering with unified and discriminative learning publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2020.3025666 – volume: volume 855 start-page: 715 year: 2018 ident: 10.1016/j.knosys.2025.114158_bib0025 article-title: Clustering of propositions equipped with uncertainty – volume: 267 year: 2023 ident: 10.1016/j.knosys.2025.114158_bib0041 article-title: Inclusivity induced adaptive graph learning for multi-view clustering publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2023.110424 – volume: 32 start-page: 4038 issue: 7 year: 2024 ident: 10.1016/j.knosys.2025.114158_bib0048 article-title: Unsupervised domain adaptation enhanced by fuzzy prompt learning publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2024.3389705 – start-page: 8012 year: 2021 ident: 10.1016/j.knosys.2025.114158_bib0006 article-title: Multimodal clustering networks for self-supervised learning from unlabeled videos – start-page: 1083 year: 2015 ident: 10.1016/j.knosys.2025.114158_bib0035 article-title: On deep multi-view representation learning – volume: 27 start-page: 1501 issue: 3 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0049 article-title: Auto-weighted multi-view learning for image clustering and semi-supervised classification publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2754939 – start-page: 2971 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0032 article-title: Multiple medoids based multi-view relational fuzzy clustering with minimax optimization – start-page: 3964 year: 2020 ident: 10.1016/j.knosys.2025.114158_bib0018 article-title: Multi-view non-negative matrix factorization discriminant learning via cross entropy loss – start-page: 4701 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0010 article-title: Multi-view multi-label canonical correlation analysis for cross-modal matching and retrieval – start-page: 2564 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0056 article-title: Self-weighted multiview clustering with multiple graphs – volume: 10 start-page: 191 issue: 2-3 year: 1984 ident: 10.1016/j.knosys.2025.114158_bib0054 article-title: Fcm: the fuzzy c-means clustering algorithm publication-title: Comput. Geosci. doi: 10.1016/0098-3004(84)90020-7 – volume: 586 start-page: 224 year: 2022 ident: 10.1016/j.knosys.2025.114158_bib0039 article-title: Multi-view fuzzy clustering of deep random walk and sparse low-rank embedding publication-title: Inform. Sci. doi: 10.1016/j.ins.2021.11.075 – volume: 11 start-page: 1276 issue: 7 year: 2009 ident: 10.1016/j.knosys.2025.114158_bib0053 article-title: Character identification in feature-length films using global face-name matching publication-title: IEEE Trans. Multimedia doi: 10.1109/TMM.2009.2030629 – start-page: 4279 year: 2017 ident: 10.1016/j.knosys.2025.114158_bib0023 article-title: Latent multi-view subspace clustering – start-page: 8172 year: 2019 ident: 10.1016/j.knosys.2025.114158_bib0047 article-title: Reciprocal multi-layer subspace learning for multi-view clustering – volume: 1 start-page: 83 issue: 2 year: 2018 ident: 10.1016/j.knosys.2025.114158_bib0004 article-title: Multi-view clustering: a survey publication-title: Big Data Mining Anal. doi: 10.26599/BDMA.2018.9020003 |
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