{"type":"ScholarlyArticle","title":"Fast Unpaired Multi-view Clustering","authors":[{"name":"Xingfeng Li","slug":"xingfeng-li","given":"Xingfeng","family":"Li","affiliation":"Department of Computer Science , Nanjing University of Science and Technology; Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore; Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Yuangang Pan","slug":"yuangang-pan","given":"Yuangang","family":"Pan","affiliation":"Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore; Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Yinghui Sun","slug":"yinghui-sun","given":"Yinghui","family":"Sun","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Quansen Sun","slug":"quansen-sun","given":"Quansen","family":"Sun","affiliation":"Department of Computer Science , Nanjing University of Science and Technology"},{"name":"Ivor Tsang","slug":"ivor-tsang","given":"Ivor","family":"Tsang","affiliation":"Centre for Frontier AI Research , Agency for Science, Technology and Research , Singapore; Institute of High Performance Computing , Agency for Science, Technology and Research , Singapore"},{"name":"Zhenwen Ren","slug":"zhenwen-ren","given":"Zhenwen","family":"Ren","affiliation":"School of National Defence Science and Technology , Southwest University of Science and Technology {lixingfeng ,"}],"abstract":"Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection and storage processes. Addressing unpaired large-scale multi-view data through anchor learning remains a research gap. The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. Specifically, FUMC first designs an inverse local manifold learning paradigm to guide the learned anchors for effective pairing and balancing, ensuring alignment, fairness, and power in unpaired multi-view data. Meanwhile, a novel bipartite graph matching framework is developed to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. The efficacy, efficiency, and superiority of our FUMC are corroborated through extensive evaluations on numerous benchmark datasets with shallow and deep SOTA methods.","doi":"10.24963/ijcai.2024/496","url":"https://ijcai.org/proceedings/2024/496","pdf":"https://ijcai.org/proceedings/2024/0496.pdf","markdown":"https://ijcai.org/proceedings/2024/496.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/496","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":496,"pages":{"first":4488,"last":4496},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2022] Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, and Zhao Zhang. Efficient deep embedded subspace clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 1-10, 2022.","title":"Efficient deep embedded subspace clustering","authors":"Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cai et al., 2024] Jinyu Cai, Yunhe Zhang, Shiping Wang, Ji- cong Fan, and Wenzhong Guo. Wasserstein embedding learning for deep clustering: A generative approach. IEEE Transactions on Multimedia, 2024.","title":"Wasserstein embedding learning for deep clustering: A generative approach","authors":"Jinyu Cai, Yunhe Zhang, Shiping Wang, Jicong Fan, Wenzhong Guo","year":"2024","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2022] Man-Sheng Chen, Chang-Dong Wang, Dong Huang, Jian-Huang Lai, and Philip S Yu. Efficient orthogonal multi-view subspace clustering. In Proceed- ings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 127-135, 2022.","title":"Efficient orthogonal multi-view subspace clustering","authors":"Chen","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2023a] Jie Chen, Hua Mao, Wai Lok Woo, and Xi Peng. Deep multiview clustering by contrasting clus- ter assignments. Proceedings of the IEEE International Conference on computer vision, 2023.","title":"Deep multiview clustering by contrasting cluster assignments","authors":"Chen","year":"2023","venue":"Proceedings of the IEEE International Conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2023b] Zhaoliang Chen, Lele Fu, Jie Yao, Wen- zhong Guo, Claudia Plant, and Shiping Wang. Learnable graph convolutional network and feature fusion for multi- view learning. Information Fusion, 95:109-119, 2023.","title":"Learnable graph convolutional network and feature fusion for multiview learning","authors":"Chen","year":"2023","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2023c] Zhe Chen, Xiao-Jun Wu, Tianyang Xu, and Josef Kittler. Fast self-guided multi-view subspace clustering. IEEE Transactions on Image Processing, pages 1-1, 2023.","title":"Fast self-guided multi-view subspace clustering","authors":"Chen","year":"2023","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2024] Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant, Shiping Wang, and Wenzhong Guo. Attributed multi-order graph convolutional network for heterogeneous graphs. Neural Networks, page 106225, 2024.","title":"Attributed multi-order graph convolutional network for heterogeneous graphs","authors":"Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant, Shiping Wang, Wenzhong Guo","year":"2024","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dong et al., 2023] Zhibin Dong, Siwei Wang, Jiaqi Jin, Xinwang Liu, and En Zhu. Cross-view topology based consistent and complementary information for deep multi- view clustering. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 19440- 19451, 2023.","title":"Cross-view topology based consistent and complementary information for deep multiview clustering","authors":"Zhibin Dong, Siwei Wang, Jiaqi Jin, Xinwang Liu, En Zhu","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2020] Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, and Pu Zhang. Tensor-svd based graph learn- ing for multi-view subspace clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3930-3937, 2020.","title":"Tensor-svd based graph learning for multi-view subspace clustering","authors":"Gao","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2020] Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, and Xi Peng. Partially view-aligned clustering. Advances in Neural Information Processing Systems, 33:2892-2902, 2020.","title":"Partially view-aligned clustering","authors":"Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ji and Feng, 2023] Jintian Ji and Songhe Feng. Anchor structure regularization induced multi-view subspace clus- tering via enhanced tensor rank minimization. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 19343-19352, 2023.","title":"Anchor structure regularization induced multi-view subspace clustering via enhanced tensor rank minimization","authors":"Ji, Feng ;, Jintian Ji, Songhe Feng","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kang et al., 2020] Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, and Zenglin Xu. Large- scale multi-view subspace clustering in linear time. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4412-4419, 2020.","title":"Largescale multi-view subspace clustering in linear time","authors":"Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kang et al., 2021] Zhao Kang, Zhiping Lin, Xiaofeng Zhu, and Wenbo Xu. Structured graph learning for scalable sub- space clustering: From single view to multiview. IEEE Transactions on Cybernetics, 52(9):8976-8986, 2021.","title":"Structured graph learning for scalable subspace clustering: From single view to multiview","authors":"Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu","year":"2021","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2022] Xingfeng Li, Yinghui Sun, Quansen Sun, and Zhenwen Ren. Consensus cluster center guided la- tent multi-kernel clustering. IEEE Transactions on Cir- cuits and Systems for Video Technology, pages 1-1, 2022.","title":"Consensus cluster center guided latent multi-kernel clustering","authors":"Li","year":"2022","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2023a] Haoran Li, Yulan Guo, Zhenwen Ren, F Richard Yu, Jiali You, and Xiaojian You. Explicit local coupling global structure clustering. IEEE Transactions on Circuits and Systems for Video Technology, 2023.","title":"Explicit local coupling global structure clustering","authors":"Li","year":"2023","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2023b] Liang Li, Junpu Zhang, Siwei Wang, Xin- wang Liu, Kenli Li, and Keqin Li. Multi-view bipartite graph clustering with coupled noisy feature filter. IEEE Transactions on Knowledge and Data Engineering, 2023.","title":"Multi-view bipartite graph clustering with coupled noisy feature filter","authors":"Li","year":"2023","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2023c] Xingfeng Li, Zhenwen Ren, Quansen Sun, and Zhi Xu. Auto-weighted tensor schatten p-norm for robust multi-view graph clustering. Pattern Recognition, 134:109083, 2023.","title":"Auto-weighted tensor schatten p-norm for robust multi-view graph clustering","authors":"Li","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2023d] Xingfeng Li, Yinghui Sun, Quansen Sun, Zhenwen Ren, and Yuan Sun. Cross-view graph matching guided anchor alignment for incomplete multi-view clus- tering. Information Fusion, page 101941, 2023.","title":"Cross-view graph matching guided anchor alignment for incomplete multi-view clustering","authors":"Li","year":"2023","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2024] Liang Li, Yuangang Pan, Jie Liu, Yue Liu, Xinwang Liu, Kenli Li, Ivor W Tsang, and Keqin Li. Bgae: Auto-encoding multi-view bipartite graph cluster- ing. IEEE Transactions on Knowledge and Data Engi- neering, 2024.","title":"Bgae: Auto-encoding multi-view bipartite graph clustering","authors":"Liang Li, Yuangang Pan, Jie Liu, Yue Liu, Xinwang Liu, Kenli Li, Ivor W Tsang, Keqin Li","year":"2024","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lin et al., 2022] Jia-Qi Lin, Man-Sheng Chen, Chang-Dong Wang, and Haizhang Zhang. A tensor approach for uncou- pled multiview clustering. IEEE Transactions on Cyber- netics, 2022.","title":"A tensor approach for uncoupled multiview clustering","authors":"Jia-Qi Lin, Man-Sheng Chen, Chang-Dong Wang, Haizhang Zhang","year":"2022","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lin et al., 2023] Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. Dual contrastive prediction for incomplete multi-view representation learn- ing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4447-4461, 2023.","title":"Dual contrastive prediction for incomplete multi-view representation learning","authors":"Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, Xi Peng","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lu et al., 2023] Zhoumin Lu, Shiping Wang, Genggeng Liu, and Feiping Nie. Robust weighted co-clustering with global and local discrimination. Pattern Recognition, 138:109405, 2023.","title":"Robust weighted co-clustering with global and local discrimination","authors":"Lu","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ng et al., 2002] Andrew Y Ng, Michael I Jordan, and Yair Weiss. On spectral clustering: Analysis and an algo- rithm. In Advances in neural information processing sys- tems, pages 849-856, 2002.","title":"On spectral clustering: Analysis and an algorithm","authors":"Y Andrew, Michael I Ng, Yair Jordan, Weiss","year":"2002","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nie et al., 2016] Feiping Nie, Jing Li, Xuelong Li, et al. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In The Twenty-Fifth International Joint Conference on Artificial Intelligence, pages 1881-1887, 2016.","title":"Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification","authors":"Nie","year":"2016","venue":"The Twenty-Fifth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2016/269"},{"position":25,"raw":"[Sun et al., 2023a] Yuan Sun, Zhenwen Ren, Peng Hu, Dezhong Peng, and Xu Wang. Hierarchical consensus hashing for cross-modal retrieval. IEEE Transactions on Multimedia, 2023.","title":"Hierarchical consensus hashing for cross-modal retrieval","authors":"Sun","year":"2023","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sun et al., 2023b] Yuan Sun, Xu Wang, Dezhong Peng, Zhenwen Ren, and Xiaobo Shen. Hierarchical hashing learning for image set classification. IEEE Transactions on Image Processing, 32:1732-1744, 2023.","title":"Hierarchical hashing learning for image set classification","authors":"Sun","year":"2023","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tang and Liu, 2022] Huayi Tang and Yong Liu. Deep safe incomplete multi-view clustering: Theorem and algo- rithm. In International Conference on Machine Learning, pages 21090-21110. PMLR, 2022.","title":"Deep safe incomplete multi-view clustering: Theorem and algorithm","authors":"Liu ; ] Huayi Tang, Yong Tang, Liu","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2021] Siwei Wang, Xinwang Liu, Xinzhong Zhu, Pei Zhang, Yi Zhang, Feng Gao, and En Zhu. Fast parameter-free multi-view subspace clustering with con- sensus anchor guidance. IEEE Transactions on Image Pro- cessing, 31:556-568, 2021.","title":"Fast parameter-free multi-view subspace clustering with consensus anchor guidance","authors":"Siwei Wang, Xinwang Liu, Xinzhong Zhu, Pei Zhang, Yi Zhang, Feng Gao, En Zhu","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2022] Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, and En Zhu. Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences. Advances in Neu- ral Information Processing Systems, 35:5882-5895, 2022.","title":"Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences","authors":"Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wen et al., 2022] Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. A survey on incomplete multi-view clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022.","title":"A survey on incomplete multi-view clustering","authors":"Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, Jinxing Li","year":"2022","venue":"IEEE Transactions on Systems, Man, and Cybernetics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wu et al., 2019] Jianlong Wu, Zhouchen Lin, and Hong- bin Zha. Essential tensor learning for multi-view spec- tral clustering. IEEE Transactions on Image Processing, 28(12):5910-5922, 2019.","title":"Essential tensor learning for multi-view spectral clustering","authors":"Jianlong Wu, Zhouchen Lin, Hongbin Zha","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xia et al., 2014] Rongkai Xia, Yan Pan, Lei Du, and Jian Yin. Robust multi-view spectral clustering via low-rank and sparse decomposition. In Proceedings of the twenty- eighth AAAI conference on artificial intelligence, pages 2149-2155, 2014.","title":"Robust multi-view spectral clustering via low-rank and sparse decomposition","authors":"Xia","year":"2014","venue":"Proceedings of the twentyeighth AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xia et al., 2022] Wei Xia, Quanxue Gao, Qianqian Wang, and Xinbo Gao. Tensor completion-based incomplete multiview clustering. IEEE Transactions on Cybernetics, 52(12):13635-13644, 2022.","title":"Tensor completion-based incomplete multiview clustering","authors":"Wei Xia, Quanxue Gao, Qianqian Wang, Xinbo Gao","year":"2022","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xie et al., 2018] Yuan Xie, Dacheng Tao, Wensheng Zhang, Yan Liu, Lei Zhang, and Yanyun Qu. On unifying multi- view self-representations for clustering by tensor multi- rank minimization. International Journal of Computer Vi- sion, 126(11):1157-1179, 2018.","title":"On unifying multiview self-representations for clustering by tensor multi-rank minimization","authors":"Yuan Xie, Dacheng Tao, Wensheng Zhang, Yan Liu, Lei Zhang, Yanyun Qu","year":"2018","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xu et al., 2022] Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. Multi-level feature learning for contrastive multi-view clustering. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16051-16060, 2022.","title":"Multi-level feature learning for contrastive multi-view clustering","authors":"Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yang et al., 2021] Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, and Xi Peng. Partially view- aligned representation learning with noise-robust con- trastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1134- 1143, 2021.","title":"Partially viewaligned representation learning with noise-robust contrastive loss","authors":"Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, Xi Peng","year":"2021","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yang et al., 2023] Mouxing Yang, Yunfan Li, Peng Hu, Jin- feng Bai, Jiancheng Lv, and Xi Peng. Robust multi- view clustering with incomplete information. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 45(1):1055-1069, 2023.","title":"Robust multiview clustering with incomplete information","authors":"Yang","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yao et al., 2023] Yinghua Yao, Yuangang Pan, Jing Li, Ivor W Tsang, and Xin Yao. Sanitized clustering against confounding bias. Machine Learning, pages 1-20, 2023.","title":"Sanitized clustering against confounding bias","authors":"Yinghua Yao, Yuangang Pan, Jing Li, Ivor W Tsang, Xin Yao","year":"2023","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[You et al., 2023] Jiali You, Zhenwen Ren, Xiaojian You, Haoran Li, and Yuancheng Yao. Priori anchor labels su- pervised scalable multi-view bipartite graph clustering. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 37, pages 10972-10979, 2023.","title":"Priori anchor labels supervised scalable multi-view bipartite graph clustering","authors":"Jiali You, Zhenwen Ren, Xiaojian You, Haoran Li, Yuancheng Yao","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yu et al., 2021] Hong Yu, Jia Tang, Guoyin Wang, and Xinbo Gao. A novel multi-view clustering method for unknown mapping relationships between cross-view sam- ples. In Proceedings of the 27th ACM SIGKDD Con- ference on Knowledge Discovery & Data Mining, pages 2075-2083, 2021.","title":"A novel multi-view clustering method for unknown mapping relationships between cross-view samples","authors":"Yu","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2015] Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, and Xiaochun Cao. Low-rank tensor con- strained multiview subspace clustering. In Proceedings of the IEEE international conference on computer vision, pages 1582-1590, 2015.","title":"Low-rank tensor constrained multiview subspace clustering","authors":"Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao","year":"2015","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2020] Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, and Dong Xu. Generalized latent multi-view subspace clustering. IEEE Transactions on Pattern Analysis and Machine In- telligence, 42(1):86-99, 2020.","title":"Generalized latent multi-view subspace clustering","authors":"Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, Dong Xu","year":"2020","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2022] Chao Zhang, Huaxiong Li, Chunlin Chen, Yuhua Qian, and Xianzhong Zhou. Enhanced group sparse regularized nonconvex regression for face recogni- tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2438-2452, 2022.","title":"Enhanced group sparse regularized nonconvex regression for face recognition","authors":"Chao Zhang, Huaxiong Li, Chunlin Chen, Yuhua Qian, Xianzhong Zhou","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2023] Chao Zhang, Huaxiong Li, Wei Lv, Zizheng Huang, Yang Gao, and Chunlin Chen. Enhanced tensor low-rank and sparse representation recovery for in- complete multi-view clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9):11174-11182, Jun. 2023.","title":"Enhanced tensor low-rank and sparse representation recovery for incomplete multi-view clustering","authors":"Chao Zhang, Huaxiong Li, Wei Lv, Zizheng Huang, Yang Gao, Chunlin Chen","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}