Abstract
Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains. By integrating information from diverse perspectives, multi-view learning methods effectively enhance accuracy, robustness, and generalization capabilities. The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses, namely multi-view classification approaches, multi-view semi-supervised classification approaches, multi-view clustering approaches, and multi-view semi-supervised clustering approaches. Despite its potential advantages, multi-view learning poses several challenges, including view inconsistency, view complementarity, optimal view fusion, the curse of dimensionality, scalability, limited labels, and generalization across domains. Nevertheless, these challenges have not discouraged researchers from exploring the potential of multiview learning. It continues to be an active and promising research area, capable of effectively addressing complex real-world problems.
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Wei W, Dai Q, Wong Y, Hu Y, Kankanhalli M, Geng W. Surface-electromyography-based gesture recognition by multi-view deep learning. IEEE Transactions on Biomedical Engineering, 2019, 66(10): 2964–2973
Tian X, Deng Z, Ying W, Choi K S, Wu D, Qin B, Wang J, Shen H, Wang S. Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(10): 1962–1972
Kong Y, Ding Z, Li J, Fu Y. Deeply learned view-invariant features for cross-view action recognition. IEEE Transactions on Image Processing, 2017, 26(6): 3028–3037
Sun S, Dong W, Liu Q. Multi-view representation learning with deep Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(12): 4453–4468
Zhang C, Cheng J, Tian Q. Multi-view image classification with visual, semantic and view consistency. IEEE Transactions on Image Processing, 2020, 29: 617–627
Xu C, Tao D, Xu C. A survey on multi-view learning. 2013, arXiv preprint arXiv: 1304.5634
Luo S, Zhang C, Zhang W, Cao X. Consistent and specific multi-view subspace clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018
Wang X, Guo X, Lei Z, Zhang C, Li S Z. Exclusivity-consistency regularized multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, 1–9
Liang Y, Huang D, Wang C D. Consistency meets inconsistency: A unified graph learning framework for multi-view clustering. In: Proceedings of the IEEE International Conference on Data Mining. 2019, 1204–1209
Li Y, Yang M, Zhang Z. A survey of multi-view representation learning. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(10): 1863–1883
Li X, Liu B, Zhang K, Chen H, Cao W, Liu W, Tao D. Multi-view learning for hyperspectral image classification: an overview. Neurocomputing, 2022, 500: 499–517
Zhao J, Xie X, Xu X, Sun S. Multi-view learning overview: Recent progress and new challenges. Information Fusion, 2017, 38: 43–54
Yan X, Hu S, Mao Y, Ye Y, Yu H. Deep multi-view learning methods: A review. Neurocomputing, 2021, 448: 106–129
Yang Y, Wang H. Multi-view clustering: a survey. Big Data Mining and Analytics, 2018, 1(2): 83–107
Fu L, Lin P, Vasilakos A V, Wang S. An overview of recent multiview clustering. Neurocomputing, 2020, 402: 148–161
Wen J, Zhang Z, Fei L, Zhang B, Xu Y, Zhang Z, Li J. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(2): 1136–1149
Chao G, Sun S, Bi J. A survey on multiview clustering. IEEE Transactions on Artificial Intelligence, 2021, 2(2): 146–168
Fang U, Li M, Li J, Gao L, Jia T, Zhang Y. A comprehensive survey on multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12): 12350–12368
Dong X, Yu Z, Cao W, Shi Y, Ma Q. A survey on ensemble learning. Frontiers of Computer Science, 2020, 14(2): 241–258
Xu Y, Yu Z, Cao W, Chen C L P. A novel classifier ensemble method based on subspace enhancement for high-dimensional data classification. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 16–30
Jiang J, Liu F, Ng W W Y, Tang Q, Wang W, Pham Q V. Dynamic incremental ensemble fuzzy classifier for data streams in green internet of things. IEEE Transactions on Green Communications and Networking, 2022, 6(3): 1316–1329
Xu Y, Yu Z, Cao W, Chen C L P, You J. Adaptive classifier ensemble method based on spatial perception for high-dimensional data classification. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(7): 2847–2862
Yu Z, Luo P, Liu J, Wong H S, You J, Han G, Zhang J. Semi-supervised ensemble clustering based on selected constraint projection. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2394–2407
Jiang J, Liu F, Liu Y, Tang Q, Wang B, Zhong G, Wang W. A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams. Computer Communications, 2022, 194: 250–257
Yang K, Yu Z, Wen X, Cao W, Chen C L P, Wong H S, You J. Hybrid classifier ensemble for imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(4): 1387–1400
Jiang B, Xiang J, Wu X, Wang Y, Chen H, Cao W, Sheng W. Robust multi-view learning via adaptive regression. Information Sciences, 2022, 610: 916–937
Zhao L, Yang T, Zhang J, Chen Z, Yang Y, Wang Z J. Co-learning non-negative correlated and uncorrelated features for multi-view data. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(4): 1486–1496
Chen W, Yang K, Yu Z, Shi Y, Chen C L P. A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review, 2024, 57(6): 1–51
Li G, Yu Z, Yang K, Lin M, Chen C L P. Exploring feature selection with limited labels: a comprehensive survey of semi-supervised and unsupervised approaches. IEEE Transactions on Knowledge and Data Engineering, 2024, doi: https://doi.org/10.1109/TKDE.2024.3397878
Li W, Wang R, Luo X. A generalized nesterov-accelerated second-order latent factor model for high-dimensional and incomplete data. IEEE Transactions on Neural Networks and Learning Systems, 2023, doi: https://doi.org/10.1109/TNNLS.2023.3321915
Luo D, Xu H, Carin L. Differentiable hierarchical optimal transport for robust multi-view learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(6): 7293–7307
Xie X, Sun S. Multi-view support vector machines with the consensus and complementarity information. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(12): 2401–2413
Hu P, Peng D, Sang Y, Xiang Y. Multi-view linear discriminant analysis network. IEEE Transactions on Image Processing, 2019, 28(11): 5352–5365
Jia K, Lin J, Tan M, Tao D. Deep multi-view learning using neuron-wise correlation-maximizing regularizers. IEEE Transactions on Image Processing, 2019, 28(10): 5121–5134
Chao G, Sun S. Consensus and complementarity based maximum entropy discrimination for multi-view classification. Information Sciences, 2016, 367–368: 296–310
Guan Z, Zhang L, Peng J, Fan J. Multi-view concept learning for data representation. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(11): 3016–3028
Wang Q, Guo Y, Wang J, Luo X, Kong X. Multi-view analysis dictionary learning for image classification. IEEE Access, 2018, 6: 20174–20183
Liu B, Chen X, Xiao Y, Li W, Liu L, Liu C. An efficient dictionary-based multi-view learning method. Information Sciences, 2021, 576: 157–172
Jia X, Jing X Y, Sun Q, Chen S, Du B, Zhang D. Human collective intelligence inspired multi-view representation learning—Enabling view communication by simulating human communication mechanism. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 46(6): 7412–7429
Zheng Q, Zhu J, Li Z. Collaborative unsupervised multi-view representation learning. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(7): 4202–4210
Ma X, Xue S, Wu J, Yang J, Paris C, Nepal S, Sheng Q Z. Deep multi-attributed-view graph representation learning. IEEE Transactions on Network Science and Engineering, 2022, 9(5): 3762–3774
Huang Z, Zhou J T, Zhu H, Zhang C, Lv J, Peng X. Deep spectral representation learning from multi-view data. IEEE Transactions on Image Processing, 2021, 30: 5352–5362
Yang S, Li L, Wang S, Zhang W, Huang Q, Tian Q. SkeletonNet: A hybrid network with a skeleton-embedding process for multi-view image representation learning. IEEE Transactions on Multimedia, 2019, 21(11): 2916–2929
Zhang D, Yang G, Zhao S, Zhang Y, Ghista D, Zhang H, Li S. Direct quantification of coronary artery stenosis through hierarchical attentive multi-view learning. IEEE Transactions on Medical Imaging, 2020, 39(12): 4322–4334
Lyu Z, Yang M, Li H. Multi-view group representation learning for location-aware group recommendation. Information Sciences, 2021, 580: 495–509
Tan Y, Zhao G. Multi-view representation learning with Kolmogorov-Smirnov to predict default based on imbalanced and complex dataset. Information Sciences, 2022, 596: 380–394
Qin Y, Qin C, Zhang X, Qi D, Feng G. NIM-Nets: noise-aware incomplete multi-view learning networks. IEEE Transactions on Image Processing, 2023, 32: 175–189
Lin Y, Gou Y, Liu X, Bai J, Lv J, Peng X. Dual contrastive prediction for incomplete multi-view representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 4447–4461
Xu C, Tao D, Xu C. Multi-view learning with incomplete views. IEEE Transactions on Image Processing, 2015, 24(12): 5812–5825
Zhu P, Yao X, Wang Y, Cao M, Hui B, Zhao S, Hu Q. Latent heterogeneous graph network for incomplete multi-view learning. IEEE Transactions on Multimedia, 2023, 25: 3033–3045
Wen J, Liu C, Deng S, Liu Y, Fei L, Yan K, Xu Y. Deep double incomplete multi-view multi-label learning with incomplete labels and missing views. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(8): 11396–11408
Liu C, Wen J, Luo X, Huang C, Wu Z, Xu Y. DICNet: deep instance-level contrastive network for double incomplete multi-view multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2023, 8807–8815
Li X, Chen S. A concise yet effective model for non-aligned incomplete multi-view and missing multi-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10): 5918–5932
Deng J, Chen X, Jiang R, Song X, Tsang I W. A multi-view multi-task learning framework for multi-variate time series forecasting. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8): 7665–7680
He J, Lawrence R. A graph-based framework for multi-task multi-view learning. In: Proceedings of the 28th International Conference on Machine Learning. 2011, 25–32
Zhang J, Huan J. Inductive multi-task learning with multiple view data. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 543–551
Zhao D, Gao Q, Lu Y, Sun D. Non-aligned multi-view multi-label classification via learning view-specific labels. IEEE Transactions on Multimedia, 2023, 25: 7235–7247
Zhang Y, Wu J, Cai Z, Yu P S. Multi-view multi-label learning with sparse feature selection for image annotation. IEEE Transactions on Multimedia, 2020, 22(11): 2844–2857
Yuan J, Liu W, Gu Z, Feng S. A unified framework for graph-based multi-view partial multi-label learning. IEEE Access, 2023, 11: 49205–49215
Liu B, Li W, Xiao Y, Chen X, Liu L, Liu C, Wang K, Sun P. Multiview multi-label learning with high-order label correlation. Information Sciences, 2023, 624: 165–184
Li B, Yuan C, Xiong W, Hu W, Peng H, Ding X, Maybank S. Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2554–2560
Xu C, Tao D, Xu C. Multi-view intact space learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(12): 2531–2544
Hu J, Lu J, Tan Y P. Sharable and individual multi-view metric learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(9): 2281–2288
Wu S, Wu A, Zheng W S. Online multi-view learning with knowledge registration units. IEEE Transactions on Neural Networks and Learning Systems, 2023, doi: https://doi.org/10.1109/TNNLS.2023.3256390
Fan R, Ouyang X, Luo T, Hu D, Hou C. Incomplete multi-view learning under label shift. IEEE Transactions on Image Processing, 2023, 32: 3702–3716
Fu Y, Hospedales T M, Xiang T, Gong S. Transductive multi-view zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(11): 2332–2345
Shi Z, Chen X, Zhao C, He H, Stuphorn V, Wu D. Multi-view broad learning system for primate oculomotor decision decoding. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(9): 1908–1920
Yan W, Li Y, Yang M. Towards deeper match for multi-view oriented multiple kernel learning. Pattern Recognition, 2023, 134: 109119
Huang S, Shi W, Xu Z, Tsang I W, Lv J. Efficient federated multiview learning. Pattern Recognition, 2022, 131: 108817
Nie F, Cai G, Li J, Li X. Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, 2018, 27(3): 1501–1511
Nie F, Li J, Li X. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 1881–1887
Nie F, Tian L, Wang R, Li X. Multiview semi-supervised learning model for image classification. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(12): 2389–2400
Xu X, Li W, Xu D, Tsang I W. Co-labeling for multi-view weakly labeled learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(6): 1113–1125
Wang X, Fu L, Zhang Y, Wang Y, Li Z. MMatch: Semi-supervised discriminative representation learning for multi-view classification. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(9): 6425–6436
Chao G, Sun S. Semi-supervised multi-view maximum entropy discrimination with expectation Laplacian regularization. Information Fusion, 2019, 45: 296–306
Zhang B, Qiang Q, Wang F, Nie F. Fast multi-view semi-supervised learning with learned graph. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1): 286–299
Huang A, Wang Z, Zheng Y, Zhao T, Lin C W. Embedding regularizer learning for multi-view semi-supervised classification. IEEE Transactions on Image Processing, 2021, 30: 6997–7011
Wang S, Chen Z, Du S, Lin Z. Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5042–5055
Qian B, Wang X, Ye J, Davidson I. A reconstruction error based framework for multi-label and multi-view learning. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(3): 594–607
Zheng F, Liu Z, Chen Y, An J, Zhang Y. A novel adaptive multi-view non-negative graph semi-supervised ELM. IEEE Access, 2020, 8: 116350–116362
Guo W, Wang Z, Du W. Robust semi-supervised multi-view graph learning with sharable and individual structure. Pattern Recognition, 2023, 140: 109565
Li Z, Qiang Q, Zhang B, Wang F, Nie F. Flexible multi-view semi-supervised learning with unified graph. Neural Networks, 2021, 142: 92–104
Jia X, Jing X Y, Zhu X, Chen S, Du B, Cai Z, He Z, Yue D. Semi-supervised multi-view deep discriminant representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(7): 2496–2509
Cui X, Huang J, Chien J T. Multi-view and multi-objective semi-supervised learning for hmm-based automatic speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(7): 1923–1935
Thammasorn P, Chaovalitwongse W A, Hippe D S, Wootton L S, Ford E C, Spraker M B, Combs S E, Peeken J C, Nyflot M J. Nearest neighbor-based strategy to optimize multi-view triplet network for classification of small-sample medical imaging data. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(2): 586–600
Xie Y, Lin B, Qu Y, Li C, Zhang W, Ma L, Wen Y, Tao D. Joint deep multi-view learning for image clustering. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(11): 3594–3606
Liang Y, Huang D, Wang C D, Yu P S. Multi-view graph learning by joint modeling of consistency and inconsistency. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 2848–2862
Huang L, Lu J, Tan Y P. Co-learned multi-view spectral clustering for face recognition based on image sets. IEEE Signal Processing Letters, 2014, 21(7): 875–879
Tang C, Zheng X, Liu X, Zhang W, Zhang J, Xiong J, Wang L. Cross-view locality preserved diversity and consensus learning for multiview unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4705–4716
Nie F, Shi S, Li J, Li X. Implicit weight learning for multi-view clustering. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(8): 4223–4236
Zhao L, Zhao T, Sun T, Liu Z, Chen Z. Multi-view robust feature learning for data clustering. IEEE Signal Processing Letters, 2020, 27: 1750–1754
Liu B Y, Huang L, Wang C D, Lai J H, Yu P S. Multi-view consensus proximity learning for clustering. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(7): 3405–3417
Hou C, Nie F, Tao H, Yi D. Multi-view unsupervised feature selection with adaptive similarity and view weight. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(9): 1998–2011
Hu S, Lou Z, Ye Y. View-wise versus cluster-wise weight: Which is better for multi-view clustering? IEEE Transactions on Image Processing, 2022, 31: 58–71
Deng Z, Liu R, Xu P, Choi K S, Zhang W, Tian X, Zhang T, Liang L, Qin B, Wang S. Multi-view clustering with the cooperation of visible and hidden views. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(2): 803–815
Yu X, Liu H, Lin Y, Liu N, Sun S. Sample-level weights learning for multi-view clustering on spectral rotation. Information Sciences, 2023, 619: 38–51
Liang C, Wang L, Liu L, Zhang H, Guo F. Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning. Pattern Recognition, 2023, 141: 109632
Dai D, Yu Z, Huang W, Hu Y, Chen C L P. Multi-objective cluster ensemble based on filter refinement scheme. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8): 8257–8269
Yu Z, Kuang Z, Liu J, Chen H, Zhang J, You J, Wong H S, Han G. Adaptive ensembling of semi-supervised clustering solutions. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1577–1590
Shi Y, Yu Z, Chen C L P, Zeng H. Consensus clustering with co-association matrix optimization. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3): 4192–4205
Yu Z, Wang D, Meng X B, Chen C L P. Clustering ensemble based on hybrid multiview clustering. IEEE Transactions on Cybernetics, 2022, 52(7): 6518–6530
Chen J, Yang S, Wang Z. Multi-view representation learning for data stream clustering. Information Sciences, 2022, 613: 731–746
Zhao H, Li Z, Chen W, Zheng Z, Xie S. Accelerated partially shared dictionary learning with differentiable scale-invariant sparsity for multi-view clustering. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 8825–8839
Zheng Q, Zhu J, Li Z, Tang H. Graph-guided unsupervised multiview representation learning. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(1): 146–159
Zheng Q. Large-scale multi-view clustering via fast essential subspace representation learning. IEEE Signal Processing Letters, 2022, 29: 1893–1897
Zhang C, Fu H, Liu S, Liu G, Cao X. Low-rank tensor constrained multiview subspace clustering. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 1582–1590
Cao X, Zhang C, Fu H, Liu S, Zhang H. Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 586–594
Zhang C, Hu Q, Fu H, Zhu P, Cao X. Latent multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, 4333–4341
Chao G, Sun J, Lu J, Wang A L, Langleben D D, Li C S, Bi J. Multiview cluster analysis with incomplete data to understand treatment effects. Information Sciences, 2019, 494: 278–293
Chao G, Wang S, Yang S, Li C, Chu D. Incomplete multi-view clustering with multiple imputation and ensemble clustering. Applied Intelligence, 2022, 52(13): 14811–14821
Fang X, Hu Y, Zhou P, Wu D O. V3H: View variation and view heredity for incomplete multiview clustering. IEEE Transactions on Artificial Intelligence, 2020, 1(3): 233–247
Yang M, Li Y, Hu P, Bai J, Lv J, Peng X. Robust multi-view clustering with incomplete information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(1): 1055–1069
Liu S, Liu X, Wang S, Niu X, Zhu E. Fast incomplete multi-view clustering with view-independent anchors. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(6): 7740–7751
Zhang L, Zhao Y, Zhu Z F, Shen D G, Ji S W. Multi-view missing data completion. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(7): 1296–1309
Yin M, Liu X, Wang L, He G. Learning latent embedding via weighted projection matrix alignment for incomplete multi-view clustering. Information Sciences, 2023, 634: 244–258
Shang M, Liang C, Luo J, Zhang H. Incomplete multi-view clustering by simultaneously learning robust representations and optimal graph structures. Information Sciences, 2023, 640: 119038
Chao G, Jiang Y, Chu D. Incomplete contrastive multi-view clustering with high-confidence guiding. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 11221–11229
Wang R, Wang P, Wu D, Sun Z, Nie F, Li X. Multi-view and multiorder structured graph learning. IEEE Transactions on Neural Networks and Learning Systems, 2023, doi: https://doi.org/10.1109/TNNLS.2023.3256390
Wang R, Nie F, Wang Z, Hu H, Li X. Parameter-free weighted multiview projected clustering with structured graph learning. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(10): 2014–2025
Xia W, Gao Q, Wang Q, Gao X, Ding C, Tao D. Tensorized bipartite graph learning for multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 5187–5202
Jiang G, Peng J, Wang H, Mi Z, Fu X. Tensorial multi-view clustering via low-rank constrained high-order graph learning. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(8): 5307–5318
Huang S, Tsang I W, Xu Z, Lv J. Measuring diversity in graph learning: A unified framework for structured multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(12): 5869–5883
Zhang X, Zhang X, Liu H, Liu X. Multi-task multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3324–3338
Zhang X, Zhang X, Liu H. Multi-task multi-view clustering for nonnegative data. In: Proceedings of the 24th International Conference on Artificial Intelligence. 2015, 4055–4061
Jiang Z, Liu X. Adaptive KNN and graph-based auto-weighted multiview consensus spectral learning. Information Sciences, 2022, 609: 1132–1146
Mei Y, Ren Z, Wu B, Yang T, Shao Y. Multi-order similarity learning for multi-view spectral clustering. Pattern Recognition, 2023, 137: 109264
Qin Y, Wu H, Zhang X, Feng G. Semi-supervised structured subspace learning for multi-view clustering. IEEE Transactions on Image Processing, 2022, 31: 1–14
Zhu Z, Gao Q. Semi-supervised clustering via cannot link relationship for multiview data. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(12): 8744–8755
Zhang C, Fu H, Wang J, Li W, Cao X, Hu Q. Tensorized multi-view subspace representation learning. International Journal of Computer Vision, 2020, 128(8): 2344–2361
Tang Y, Xie Y, Zhang C, Zhang W. Constrained tensor representation learning for multi-view semi-supervised subspace clustering. IEEE Transactions on Multimedia, 2022, 24: 3920–3933
Zhang T, Zheng W, Cui Z, Zong Y, Yan J, Yan K. A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Transactions on Multimedia, 2016, 18(12): 2528–2536
Wei X, Yu R, Sun J. Learning view-based graph convolutional network for multi-view 3D shape analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(6): 7525–7541
Dong C, Chen X, Hu R, Cao J, Li X. MVSS-Net: Multi-view multi-scale supervised networks for image manipulation detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(3): 3539–3553
Tran H N, Nguyen H Q, Doan H G, Tran T H, Le T L, Vu H. Pairwise-covariance multi-view discriminant analysis for robust cross-view human action recognition. IEEE Access, 2021, 9: 76097–76111
Wang Y, Xiao Y, Lu J, Tan B, Cao Z, Zhang Z, Zhou J T. Discriminative multi-view dynamic image fusion for cross-view 3-D action recognition. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5332–5345
Chen J, Wang Y, Tang Y Y. Person re-identification by exploiting spatio-temporal cues and multi-view metric learning. IEEE Signal Processing Letters, 2016, 23(7): 998–1002
Yuan Y, Xun G, Jia K, Zhang A. A multi-view deep learning framework for EEG seizure detection. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 83–94
Yang S, Lian C, Zeng Z, Xu B, Zang J, Zhang Z. A multi-view multi-scale neural network for multi-label ECG classification. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(3): 648–660
Puyol-Antón E, Ruijsink B, Gerber B, Amzulescu M S, Langet H, De Craene M, Schnabel J A, Piro P, King A P. Regional multi-view learning for cardiac motion analysis: Application to identification of dilated cardiomyopathy patients. IEEE Transactions on Biomedical Engineering, 2019, 66(4): 956–966
Zhang J, Huan J. Predicting drug-induced QT prolongation effects using multi-view learning. IEEE Transactions on NanoBioscience, 2013, 12(3): 206–213
Jin Y, Li C, Li Y, Peng P, Giannopoulos G A. Model latent views with multi-center metric learning for vehicle re-identification. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1919–1931
Zhu Y, Zhang S, Chen S. Vehicle recognition based on carrier-free UWB radars using contrastive multi-view learning. IEEE Microwave and Wireless Technology Letters, 2023, 33(3): 343–346
Ge H, Gao D, Sun L, Hou Y, Yu C, Wang Y, Tan G. Multi-agent transfer reinforcement learning with multi-view encoder for adaptive traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12572–12587
Yang H, Chu X, Zhang L, Sun Y, Li D, Maybank S J. QuadNet: Quadruplet loss for multi-view learning in baggage re-identification. Pattern Recognition, 2022, 126: 108546
Zhang X, Zong L, Liu X, Yu H. Constrained NMF-based multi-view clustering on unmapped data. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015
Huang D, Wang C D, Lai J H. Fast multi-view clustering via ensembles: Towards scalability, superiority, and simplicity. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(11): 11388–11402
Tan Q, Yu G, Domeniconi C, Wang J, Zhang Z. Incomplete multiview weak-label learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 2703–2709
Acknowledgements
This work was supported in part by the Major Key Project of PCL, China (PCL2023AS7-1 and PCL2023A09), in part by the National Key R&D Program of China (2023YFA1011601), in part by the National Natural Science Foundation of China (Grant Nos. 62106224 and U21A20478), and in part by the Guangzhou Science and Technology Plan Project (2024A04J3749).
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Zhiwen Yu is a professor in the School of Computer Science and Engineering, South China University of Technology, China. He received the PhD degree from City University of Hong Kong, China in 2008. Dr. Yu has authored or coauthored more than 200 refereed journal articles and international conference papers, including more than 70 articles in the journals of IEEE Transactions. He is an Associate Editor of the IEEE Transactions on systems, man, and cybernetics: systems. He is a senior member of IEEE and ACM, a Member of the Council of China Computer Federation (CCF).
Ziyang Dong received the BS degree from the School of Computer Science and Engineering, South China University of Technology, China in 2011, and received the MASc degree from the School of Computer Science and Engineering, South China University of Technology, China in 2016. He is currently pursuing the PhD degree in the School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. His research interests include machine learning and data mining.
Chenchen Yu received the BS degree in 2023 from the Hohai University, China and he is currently working toward the MS degree with the South China University of Technology, China. His research interests include data mining and machine learning.
Kaixiang Yang received the BS degree and MS degree from the University of Electronic Science and Technology of China and Harbin Institute of Technology, China in 2012 and 2015, respectively, and the PhD degree from the School of Computer Science and Engineering, South China University of Technology, China in 2020. He has been a Research Engineer with the 7th Research Institute, China Electronics Technology Group Corporation, China from 2015 to 2017, and has been a postdoctoral researcher with Zhejiang University, China from 2021 to 2023. He is currently an associate professor with the School of Computer Science and Engineering, South China University of Technology, China. His research interests include pattern recognition, machine learning, and industrial data intelligence.
Ziwei Fan received the BS degree of Natural Sciences from the University of Cambridge, United Kingdom in 2011. She is currently pursuing the PhD degree in the School of Computer Science and Engineering, South China University of Technology, China. Her research interests include machine learning and AI based imaging diagnosis.
C. L. Philip Chen is the Chair Professor and Dean of the College of Computer Science and Engineering, South China University of Technology, China. He is a Fellow of IEEE, AAAS, IAPR, CAA, and HKIE; a member of Academia Europaea (AE), and a member of European Academy of Sciences and Arts (EASA). He received IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learnings, and he is a highly cited researcher by Clarivate Analytics from 2018–2022. His current research interests include cybernetics, systems, and computational intelligence. He was the Editor-in-Chief of the IEEE Transactions on Cybernetics, the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, President of IEEE Systems, Man, and Cybernetics Society. Dr. Chen was a recipient of the 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University, USA in 1988, after he graduated from the University of Michigan at Ann Arbor, Ann Arbor, MI, USA in 1985.
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Yu, Z., Dong, Z., Yu, C. et al. A review on multi-view learning. Front. Comput. Sci. 19, 197334 (2025). https://doi.org/10.1007/s11704-024-40004-w
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DOI: https://doi.org/10.1007/s11704-024-40004-w