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This article addresses this gap by presenting a thorough overview and classification of MVC methods, categorizing them into seven distinct classes: text, image, time series, hyperspectral, video, signal, and 3D shape. Our meticulous examination within each class highlights advancements and evaluates their applicability in both supervised and semi-supervised learning contexts. Beyond this retrospective analysis, we explore future directions for research and development in this domain. This survey serves as a compendium of existing knowledge and as a guide for future endeavors in MVC, shaping the trajectory of ongoing research and innovation.<\/jats:p>","DOI":"10.1145\/3767728","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:17:41Z","timestamp":1758028661000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Comprehensive Survey on Multi-View Classification: Methods, Applications, and Challenges"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4459-0703","authenticated-orcid":false,"given":"Kamal","family":"Berahmand","sequence":"first","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3150-3527","authenticated-orcid":false,"given":"Fatemeh","family":"Daneshfar","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Kurdistan, Sanandaj, the Islamic Republic of Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1685-4268","authenticated-orcid":false,"given":"Maryam","family":"Rahmaninia","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Islamic Azad University Branch of Kermanshah, Kermanshah, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2080-8483","authenticated-orcid":false,"given":"Maryam","family":"Haghighat","sequence":"additional","affiliation":[{"name":"Queensland University of Technology, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-9420","authenticated-orcid":false,"given":"Mahdi","family":"Jalili","sequence":"additional","affiliation":[{"name":"School of Engineering, RMIT University, Melbourne, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.08.024"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3064974"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12115720"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.08.001"},{"issue":"2022","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"100036","DOI":"10.1016\/j.jcmds.2022.100036","article-title":"EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection","volume":"3","author":"Andresini Giuseppina","year":"2022","unstructured":"Giuseppina Andresini, Andrea Iovine, Roberto Gasbarro, Marco Lomolino, Marco de Gemmis, and Annalisa Appice. 2022. 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Retrieved from https:\/\/arxiv.org\/abs\/2404.13921"},{"issue":"2022","key":"e_1_3_1_47_2","first-page":"109363","article-title":"A survey on unsupervised learning for wearable sensor-based activity recognition","author":"Ige Ayokunle Olalekan","year":"2022","unstructured":"Ayokunle Olalekan Ige and Mohd Halim Mohd Noor. 2022. A survey on unsupervised learning for wearable sensor-based activity recognition. Applied Soft Computing. 127 (2022), 109363.","journal-title":"Applied Soft Computing"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"issue":"2021","key":"e_1_3_1_49_2","first-page":"179","article-title":"A survey: Deep learning for hyperspectral image classification with few labeled samples","volume":"448","author":"Jia Sen","year":"2021","unstructured":"Sen Jia, Shuguo Jiang, Zhijie Lin, Nanying Li, Meng Xu, and Shiqi Yu. 2021. A survey: Deep learning for hyperspectral image classification with few labeled samples. 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A survey on graph neural networks for time series: Forecasting classification imputation and anomaly detection. arXiv:2307.03759. Retrieved from https:\/\/arxiv.org\/abs\/2307.03759"},{"key":"e_1_3_1_55_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"31","author":"Jing Xiao-Yuan","year":"2017","unstructured":"Xiao-Yuan Jing, Fei Wu, Xiwei Dong, Shiguang Shan, and Songcan Chen. 2017. Semi-supervised multi-view correlation feature learning with application to webpage classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31."},{"issue":"4","key":"e_1_3_1_56_2","first-page":"e1447","article-title":"A comprehensive review on Arabic word sense disambiguation for natural language processing applications","volume":"12","author":"Kaddoura Sanaa","year":"2022","unstructured":"Sanaa Kaddoura and Rowanda D. Ahmed. 2022. A comprehensive review on Arabic word sense disambiguation for natural language processing applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12, 4 (2022), e1447.","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"e_1_3_1_57_2","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/ISTEL.2012.6483102","volume-title":"Proceedings of 6th International Symposium on Telecommunications (IST)","author":"Karimian Mahmood","year":"2012","unstructured":"Mahmood Karimian, Mostafa Tavassolipour, and Shohreh Kasaei. 2012. Exploiting multiview properties in semi-supervised video classification. In Proceedings of 6th International Symposium on Telecommunications (IST). IEEE, 837\u2013842."},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","unstructured":"Payam Karisani Negin Karisani and Li Xiong. 2021. Multi-view active learning for short text classification in user-generated data. arXiv:2112.02611. Retrieved from https:\/\/arxiv.org\/abs\/2112.02611","DOI":"10.18653\/v1\/2022.findings-emnlp.481"},{"issue":"2019","key":"e_1_3_1_59_2","first-page":"32482","article-title":"Multi-view temporal ensemble for classification of non-stationary signals","volume":"7","author":"Koh Bee Hock David","year":"2019","unstructured":"Bee Hock David Koh and Wai Lok Woo. 2019. Multi-view temporal ensemble for classification of non-stationary signals. IEEE Access 7 (2019), 32482\u201332491.","journal-title":"IEEE Access"},{"issue":"7","key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10462-024-10803-5","article-title":"Unlocking the capabilities of explainable few-shot learning in remote sensing","volume":"57","author":"Lee Gao Yu","year":"2024","unstructured":"Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, and Vu. N. Duong. 2024. Unlocking the capabilities of explainable few-shot learning in remote sensing. 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In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 989\u2013998."},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2907932"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2872063"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14091971"},{"issue":"2021","key":"e_1_3_1_70_2","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ins.2020.10.021","article-title":"Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification","volume":"548","author":"Liang Yunji","year":"2021","unstructured":"Yunji Liang, Huihui Li, Bin Guo, Zhiwen Yu, Xiaolong Zheng, Sagar Samtani, and Daniel D. Zeng. 2021. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. 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Deep multi-level fusion network for multi-source image pixel-wise classification. Knowledge-Based Systems 221 (2021), 106921.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_1_77_2","first-page":"16688","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu Xianpeng","year":"2024","unstructured":"Xianpeng Liu, Ce Zheng, Ming Qian, Nan Xue, Chen Chen, Zhebin Zhang, Chen Li, and Tianfu Wu. 2024. Multi-view attentive contextualization for multi-view 3D object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16688\u201316698."},{"key":"e_1_3_1_78_2","unstructured":"Ravikiran Mane Effie Chew Karen Chua Kai Keng Ang Neethu Robinson A. Prasad Vinod Seong-Whan Lee and Cuntai Guan. 2021. FBCNet: A multi-view convolutional neural network for brain-computer interface. arXiv:2104.01233. Retrieved from https:\/\/arxiv.org\/abs\/2104.01233"},{"issue":"4","key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1080\/01431161.2015.1007251","article-title":"Improving hyperspectral image classification by combining spectral, texture, and shape features","volume":"36","author":"Mirzapour Fardin","year":"2015","unstructured":"Fardin Mirzapour and Hassan Ghassemian. 2015. Improving hyperspectral image classification by combining spectral, texture, and shape features. 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Retrieved from https:\/\/arxiv.org\/abs\/2301.09811","DOI":"10.1016\/j.neucom.2023.126639"},{"issue":"1","key":"e_1_3_1_89_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12859-023-05140-3","article-title":"Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism","volume":"24","author":"Peng Wei","year":"2023","unstructured":"Wei Peng, Rong Wu, Wei Dai, and Ning Yu. 2023. Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism. BMC Bioinformatics 24, 1 (2023), 16.","journal-title":"BMC Bioinformatics"},{"issue":"9","key":"e_1_3_1_90_2","first-page":"5903","article-title":"2021. XSleepNet: Multi-view sequential model for automatic sleep staging","volume":"44","author":"Phan Huy","unstructured":"Huy Phan, Oliver Y. Ch\u00e9n, Minh C. Tran, and Philipp Koch, Alfred Mertins, and Maarten De Vos. 2021. 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IEEE, 611\u2013615."},{"issue":"2023","key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"103687","DOI":"10.1016\/j.cviu.2023.103687","article-title":"A multi-view-CNN framework for deep representation learning in image classification","volume":"232","author":"Pintelas Emmanuel","year":"2023","unstructured":"Emmanuel Pintelas, Ioannis E. Livieris, Sotiris Kotsiantis, and Panagiotis Pintelas. 2023. A multi-view-CNN framework for deep representation learning in image classification. Computer Vision and Image Understanding 232 (2023), 103687.","journal-title":"Computer Vision and Image Understanding"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-011-0012-5"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.609"},{"issue":"2025","key":"e_1_3_1_95_2","doi-asserted-by":"crossref","first-page":"102894","DOI":"10.1016\/j.inffus.2024.102894","article-title":"A survey on multi-view fusion for predicting links in biomedical bipartite networks: Methods and applications","volume":"117","author":"Qian Yuqing","year":"2025","unstructured":"Yuqing Qian, Yizheng Wang, Junkai Liu, Quan Zou, Yijie Ding, Xiaoyi Guo, and Weiping Ding. 2025. A survey on multi-view fusion for predicting links in biomedical bipartite networks: Methods and applications. Information Fusion 117 (2025), 102894.","journal-title":"Information Fusion"},{"issue":"2022","key":"e_1_3_1_96_2","doi-asserted-by":"crossref","first-page":"108224","DOI":"10.1016\/j.patcog.2021.108224","article-title":"Deep neural networks-based relevant latent representation learning for hyperspectral image classification","volume":"121","author":"Sellami Akrem","year":"2022","unstructured":"Akrem Sellami and Salvatore Tabbone. 2022. Deep neural networks-based relevant latent representation learning for hyperspectral image classification. Pattern Recognition 121 (2022), 108224.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_97_2","first-page":"99","volume-title":"Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018","author":"Sen Pratap Chandra","year":"2020","unstructured":"Pratap Chandra Sen, Mahimarnab Hajra, and Mitadru Ghosh. 2020. Supervised classification algorithms in machine learning: A survey and review. In Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018. Springer, 99\u2013111."},{"key":"e_1_3_1_98_2","unstructured":"Younggyo Seo Junsu Kim Stephen James Kimin Lee Jinwoo Shin and Pieter Abbeel. 2023. Multi-view masked autoencoders for visual control."},{"issue":"8","key":"e_1_3_1_99_2","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1109\/LGRS.2019.2945886","article-title":"Roof classification from 3-D LiDAR point clouds using multiview CNN with self-attention","volume":"17","author":"Shajahan Dimple A.","year":"2019","unstructured":"Dimple A. Shajahan, Vaibhav Nayel, and Ramanathan Muthuganapathy. 2019. Roof classification from 3-D LiDAR point clouds using multiview CNN with self-attention. IEEE Geoscience and Remote Sensing Letters 17, 8 (2019), 1465\u20131469.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_1_100_2","unstructured":"Shayan Sharifi. 2025. Enhancing kidney transplantation through multi-agent kidney exchange programs: A comprehensive review and optimization models. arXiv:2502.07819. Retrieved from https:\/\/arxiv.org\/abs\/2502.07819"},{"issue":"2025","key":"e_1_3_1_101_2","doi-asserted-by":"crossref","first-page":"121800","DOI":"10.1016\/j.ins.2024.121800","article-title":"Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning","volume":"699","author":"Sheikhpour Razieh","year":"2025","unstructured":"Razieh Sheikhpour, Mehrnoush Mohammadi, Kamal Berahmand, Farid Saberi-Movahed, and Hassan Khosravi. 2025. Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning. Information Sciences 699 (2025), 121800.","journal-title":"Information Sciences"},{"issue":"2017","key":"e_1_3_1_102_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","article-title":"A survey on semi-supervised feature selection methods","volume":"64","author":"Sheikhpour Razieh","year":"2017","unstructured":"Razieh Sheikhpour, Mehdi Agha Sarram, Sajjad Gharaghani, and Mohammad Ali Zare Chahooki. 2017. A survey on semi-supervised feature selection methods. Pattern Recognition 64 (2017), 141\u2013158.","journal-title":"Pattern Recognition"},{"issue":"2017","key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2016.11.013","article-title":"Discovering social spammers from multiple views","volume":"225","author":"Shen Hua","year":"2017","unstructured":"Hua Shen, Fenglong Ma, Xianchao Zhang, Linlin Zong, Xinyue Liu, and Wenxin Liang. 2017. Discovering social spammers from multiple views. Neurocomputing 225 (2017), 49\u201357.","journal-title":"Neurocomputing"},{"issue":"2022","key":"e_1_3_1_104_2","first-page":"248","article-title":"Semi-supervised learning based on intra-view heterogeneity and inter-view compatibility for image classification","volume":"488","author":"Shi Shaojun","year":"2022","unstructured":"Shaojun Shi, Feiping Nie, Rong Wang, and Xuelong Li. 2022. Semi-supervised learning based on intra-view heterogeneity and inter-view compatibility for image classification. Neurocomputing 488 (2022), 248\u2013260.","journal-title":"Neurocomputing"},{"issue":"2024","key":"e_1_3_1_105_2","first-page":"2965","article-title":"Sentiment analysis of tweets using text and graph multi-views learning","volume":"66","author":"Singh Loitongbam Gyanendro","year":"2024","unstructured":"Loitongbam Gyanendro Singh and Sanasam Ranbir Singh. 2024. Sentiment analysis of tweets using text and graph multi-views learning. Knowledge and Information Systems 66 (2024), 2965\u20132985.","journal-title":"Knowledge and Information Systems"},{"issue":"7","key":"e_1_3_1_106_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3543848","article-title":"Multimodal classification: Current landscape, taxonomy and future directions","volume":"55","author":"Sleeman William C.","year":"2022","unstructured":"William C. Sleeman, IV, Rishabh Kapoor, and Preetam Ghosh. 2022. Multimodal classification: Current landscape, taxonomy and future directions. Comput. Surveys 55, 7 (2022), 1\u201331.","journal-title":"Comput. 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A review of methods for imbalanced multi-label classification. Pattern Recognition 118 (2021), 107965.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2940485"},{"key":"e_1_3_1_115_2","first-page":"63","volume-title":"Proceedings of Conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions","author":"Toker Kemal G\u00fcrkan","year":"2019","unstructured":"Kemal G\u00fcrkan Toker and Seniha Esen Y\u00fcksel. 2019. Deep canonical correlation analysis for hyperspectral image classification. In Proceedings of Conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, Vol. 11150, SPIE, 63\u201369."},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2002.800560"},{"issue":"2021","key":"e_1_3_1_117_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2019.12.151","article-title":"Conflux LSTMs network: A novel approach for multi-view action recognition","volume":"435","author":"Ullah Amin","year":"2021","unstructured":"Amin Ullah, Khan Muhammad, Tanveer Hussain, and Sung Wook Baik. 2021. Conflux LSTMs network: A novel approach for multi-view action recognition. Neurocomputing 435 (2021), 321\u2013329.","journal-title":"Neurocomputing"},{"key":"e_1_3_1_118_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916648"},{"key":"e_1_3_1_119_2","doi-asserted-by":"crossref","unstructured":"Sanaz Vasheghani and Shayan Sharifi. 2025. Dynamic ensemble learning for robust image classification: A model-specific selection strategy. Retrieved from https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5215134","DOI":"10.2139\/ssrn.5215134"},{"key":"e_1_3_1_120_2","first-page":"427","volume-title":"Proceedings of Computer Vision (ECCV \u201920)","author":"Vyas Shruti","year":"2020","unstructured":"Shruti Vyas, Yogesh S. Rawat, and Mubarak Shah. 2020. Multi-view action recognition using cross-view video prediction. In Proceedings of Computer Vision (ECCV \u201920), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer International Publishing, Cham, 427\u2013444."},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403284"},{"key":"e_1_3_1_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3112214"},{"key":"e_1_3_1_123_2","first-page":"1083","volume-title":"Proceedings of International Conference on Machine Learning","author":"Wang Weiran","year":"2015","unstructured":"Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. 2015. On deep multi-view representation learning. In Proceedings of International Conference on Machine Learning. PMLR, 1083\u20131092."},{"issue":"7","key":"e_1_3_1_124_2","doi-asserted-by":"crossref","first-page":"2646","DOI":"10.1109\/TIP.2013.2255300","article-title":"Grassmannian regularized structured multi-view embedding for image classification","volume":"22","author":"Wang Xinchao","year":"2013","unstructured":"Xinchao Wang, Wei Bian, and Dacheng Tao. 2013. 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In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5, 401\u2013408."},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461598.3461606"},{"issue":"2022","key":"e_1_3_1_130_2","first-page":"142","article-title":"Semi-supervised multi-view graph convolutional networks with application to webpage classification","volume":"591","author":"Wu Fei","year":"2022","unstructured":"Fei Wu, Xiao-Yuan Jing, Pengfei Wei, Chao Lan, Yimu Ji, Guo-Ping Jiang, and Qinghua Huang. 2022. Semi-supervised multi-view graph convolutional networks with application to webpage classification. Information Sciences 591 (2022), 142\u2013154.","journal-title":"Information Sciences"},{"issue":"2016","key":"e_1_3_1_131_2","first-page":"143","article-title":"Multi-view low-rank dictionary learning for image classification","volume":"50","author":"Wu Fei","year":"2016","unstructured":"Fei Wu, Xiao-Yuan Jing, Xinge You, Dong Yue, Ruimin Hu, and Jing-Yu Yang. 2016. Multi-view low-rank dictionary learning for image classification. Pattern Recognition 50 (2016), 143\u2013154.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_132_2","volume-title":"MVFNet: Multi-View Fusion Network for Efficient Video Recognition","author":"Wu Wenhao","year":"2021","unstructured":"Wenhao Wu, Dongliang He, Tianwei Lin, Fu Li, Chuang Gan, and Errui Ding. 2021. MVFNet: Multi-View Fusion Network for Efficient Video Recognition. AAAI."},{"key":"e_1_3_1_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093608"},{"issue":"2019","key":"e_1_3_1_134_2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.ins.2018.12.050","article-title":"Action recognition for depth video using multi-view dynamic images","volume":"480","author":"Xiao Yang","year":"2019","unstructured":"Yang Xiao, Jun Chen, Yancheng Wang, Zhiguo Cao, Joey Tianyi Zhou, and Xiang Bai. 2019. Action recognition for depth video using multi-view dynamic images. Information Sciences 480 (2019), 287\u2013304.","journal-title":"Information Sciences"},{"key":"e_1_3_1_135_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20123496"},{"issue":"2020","key":"e_1_3_1_136_2","first-page":"102115","article-title":"A multi-view CNN-based acoustic classification system for automatic animal species identification","volume":"102","author":"Xu Weitao","year":"2020","unstructured":"Weitao Xu, Xiang Zhang, Lina Yao, Wanli Xue, and Bo Wei. 2020. A multi-view CNN-based acoustic classification system for automatic animal species identification. Ad Hoc Networks 102 (2020), 102115.","journal-title":"Ad Hoc Networks"},{"key":"e_1_3_1_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00333"},{"key":"e_1_3_1_138_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.090"},{"issue":"2019","key":"e_1_3_1_139_2","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.patcog.2018.11.015","article-title":"Adaptive-weighting discriminative regression for multi-view classification","volume":"88","author":"Yang Muli","year":"2019","unstructured":"Muli Yang, Cheng Deng, and Feiping Nie. 2019. Adaptive-weighting discriminative regression for multi-view classification. Pattern Recognition 88, (2019), 236\u2013245.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3220219"},{"key":"e_1_3_1_141_2","first-page":"1310","volume-title":"Proceedings of the 26th ACM International Conference on Multimedia","author":"You Haoxuan","year":"2018","unstructured":"Haoxuan You, Yifan Feng, Rongrong Ji, and Yue Gao. 2018. Pvnet: A joint convolutional network of point cloud and multi-view for 3d shape recognition. In Proceedings of the 26th ACM International Conference on Multimedia, 1310\u20131318."},{"key":"e_1_3_1_142_2","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Yu Tan","year":"2018","unstructured":"Tan Yu, Jingjing Meng, and Junsong Yuan. 2018. Multi-View harmonized bilinear network for 3D object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"issue":"2022","key":"e_1_3_1_143_2","first-page":"109694","article-title":"A semi-supervised label-driven auto-weighted strategy for multi-view data classification","volume":"255","author":"Yu Yuyuan","year":"2022","unstructured":"Yuyuan Yu, Guoxu Zhou, Haonan Huang, Shengli Xie, and Qibin Zhao. 2022. A semi-supervised label-driven auto-weighted strategy for multi-view data classification. Knowledge-Based Systems 255 (2022), 109694.","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"e_1_3_1_144_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/JBHI.2018.2871678","article-title":"A multi-view deep learning framework for EEG seizure detection","volume":"23","author":"Yuan Ye","year":"2018","unstructured":"Ye Yuan, Guangxu Xun, Kebin Jia, and Aidong Zhang. 2018. A multi-view deep learning framework for EEG seizure detection. IEEE Journal of Biomedical and Health Informatics 23, 1 (2018), 83\u201394.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_1_145_2","unstructured":"Donglin Zhan Shiyu Yi Dongli Xu Xiao Yu Denglin Jiang Siqi Yu Haoting Zhang Wenfang Shangguan and Weihua Zhang. 2019. Adaptive transfer learning of multi-view time series classification. arXiv:1910.07632. Retrieved from https:\/\/arxiv.org\/abs\/1910.07632"},{"issue":"2019","key":"e_1_3_1_146_2","first-page":"617","article-title":"Multi-view image classification with visual, semantic and view consistency","volume":"29","author":"Zhang Chunjie","year":"2019","unstructured":"Chunjie Zhang, Jian Cheng, and Qi Tian. 2019. Multi-view image classification with visual, semantic and view consistency. 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Retrieved from https:\/\/arxiv.org\/abs\/2310.07186"},{"issue":"3","key":"e_1_3_1_149_2","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.visinf.2021.09.003","article-title":"MV-LFN: Multi-view based local information fusion network for 3D shape recognition","volume":"5","author":"Zhang Jing","year":"2021","unstructured":"Jing Zhang, Dangdang Zhou, Yue Zhao, Weizhi Nie, and Yuting Su. 2021. MV-LFN: Multi-view based local information fusion network for 3D shape recognition. Visual Informatics 5, 3 (2021), 114\u2013119.","journal-title":"Visual Informatics"},{"issue":"2019","key":"e_1_3_1_150_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2018.11.019","article-title":"Feature selection with multi-view data: A survey","volume":"50","author":"Zhang Rui","year":"2019","unstructured":"Rui Zhang, Feiping Nie, Xuelong Li, and Xian Wei. 2019. Feature selection with multi-view data: A survey. Information Fusion 50 (2019), 158\u2013167.","journal-title":"Information Fusion"},{"key":"e_1_3_1_151_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614759"},{"issue":"2021","key":"e_1_3_1_152_2","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.ins.2021.05.044","article-title":"Learning sentiment sentence representation with multiview attention model","volume":"571","author":"Zhang You","year":"2021","unstructured":"You Zhang, Jin Wang, and Xuejie Zhang. 2021. Learning sentiment sentence representation with multiview attention model. Information Sciences 571 (2021), 459\u2013474.","journal-title":"Information Sciences"},{"key":"e_1_3_1_153_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"issue":"2025","key":"e_1_3_1_154_2","doi-asserted-by":"crossref","first-page":"101651","DOI":"10.1016\/j.iot.2025.101651","article-title":"DSEAGC: Dual-spectral embedding for attributed graph clustering","volume":"32","author":"Zhao Yuwen","year":"2025","unstructured":"Yuwen Zhao, Weifang Liang, Zhi Gong, Shibing Sun, and Shayan Nejadshams. 2025. DSEAGC: Dual-spectral embedding for attributed graph clustering. Internet of Things. 32 (2025), 101651.","journal-title":"Internet of Things"},{"key":"e_1_3_1_155_2","doi-asserted-by":"publisher","DOI":"10.1145\/3645108"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"e_1_3_1_157_2","first-page":"255","volume-title":"Proceedings of Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015","volume":"6","author":"Zhu Xiaofeng","year":"2015","unstructured":"Xiaofeng Zhu, Heung-Il Suk, Yonghua Zhu, Kim-Han Thung, Guorong Wu, and Dinggang Shen. 2015. Multi-view classification for identification of Alzheimer\u2019s disease. In Proceedings of Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, 6. 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