{"type":"ScholarlyArticle","title":"Dynamic Weighted Graph Fusion for Deep Multi-View Clustering","authors":[{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Jingyu Pu","slug":"jingyu-pu","given":"Jingyu","family":"Pu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Chenhang Cui","slug":"chenhang-cui","given":"Chenhang","family":"Cui","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Yan Zheng","slug":"yan-zheng","given":"Yan","family":"Zheng","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Xinyue Chen","slug":"xinyue-chen","given":"Xinyue","family":"Chen","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Xiaorong Pu","slug":"xiaorong-pu","given":"Xiaorong","family":"Pu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Lifang He","slug":"lifang-he","given":"Lifang","family":"He","affiliation":"Department of Computer Science and Engineering , Lehigh University , Bethlehem , USA"}],"abstract":"By exploring complex graph information hidden in data from multiple views, multi-view clustering based on graph neural network significantly enhances the clustering performance and has drawn increasing attention in recent years. Although considerable progress has been made, most existing GNN based MVC models merely consider the explicit presence of graph structure in raw data and ignore that latent graphs of different views also provide specific information for the clustering task. We propose dynamic weighted graph fusion for deep multi-view clustering (DFMVC) to address this issue. Specifically, DFMVC learns embedded features via deep autoencoders and then constructs latent graphs for each individual view. Then, it concatenates the embedded features of all views to form a global feature to leverage complementary information, as well as generates a fusion graph via combining all latent graphs to accurately capture the topological information among samples. Based on the informative fusion graph and global features, the graph convolution module is adopted to derive a representation with global comprehensive information, which is further used to generate pseudo-label information. In a self-supervised manner, such information guides each view to dynamically learn discriminative features and latent graphs. Extensive experimental results demonstrate the efficacy of DFMVC.","doi":"10.24963/ijcai.2024/535","url":"https://ijcai.org/proceedings/2024/535","pdf":"https://ijcai.org/proceedings/2024/0535.pdf","markdown":"https://ijcai.org/proceedings/2024/535.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/535","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":535,"pages":{"first":4842,"last":4850},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Bickel and Scheffer, 2004] Steffen Bickel and Tobias Scheffer. Multi-view clustering. In ICDM, pages 19-26, 2004.","title":"Multi-view clustering","authors":"Scheffer Bickel, Steffen Bickel, Tobias Scheffer","year":"2004","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bruno and Marchand-Maillet, 2009] Eric Bruno and Stéphane Marchand-Maillet. Multiview clustering: a late fusion approach using latent models. In SIGIR, pages 736-737, 2009.","title":"Multiview clustering: a late fusion approach using latent models","authors":"Eric Bruno, Stéphane Marchand-Maillet","year":"2009","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Burr et al., 2022] Viv Burr, Nigel King, and Mark Heck- mann. The qualitative analysis of repertory grid data: In- terpretive clustering. Qualitative Research in Psychology, 19(3):678-702, 2022.","title":"The qualitative analysis of repertory grid data: Interpretive clustering","authors":"Viv Burr, Nigel King, Mark Heckmann","year":"2022","venue":"Qualitative Research in Psychology","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2012] Xiao Cai, Hua Wang, Heng Huang, and Chris Ding. Joint stage recognition and anatomical an- notation of drosophila gene expression patterns. Bioinfor- matics, 28(12):i16-i24, 2012.","title":"Joint stage recognition and anatomical annotation of drosophila gene expression patterns","authors":"Xiao Cai, Hua Wang, Heng Huang, Chris Ding","year":"2012","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2020] Man-Sheng Chen, Ling Huang, Chang- Dong Wang, and Dong Huang. Multi-view clustering in latent embedding space. In AAAI, pages 3513-3520, 2020.","title":"Multi-view clustering in latent embedding space","authors":"Chen","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2022] Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, and Lifang He. Variational graph generator for multi-view graph clus- tering. arXiv preprint arXiv:2210.07011, 2022.","title":"Variational graph generator for multi-view graph clustering","authors":"Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cheng et al., 2021] Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, and Quanxue Gao. Multi-view attribute graph convolution networks for clustering. In IJCAI, pages 2973-2979, 2021.","title":"Multi-view attribute graph convolution networks for clustering","authors":"Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, Quanxue Gao","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/411"},{"position":8,"raw":"[Cui et al., 2024] Chenhang Cui, Yazhou Ren, Jingyu Pu, Ji- awei Li, Xiaorong Pu, Tianyi Wu, Yutao Shi, and Lifang He. A novel approach for effective multi-view clustering with information-theoretic perspective. Advances in Neu- ral Information Processing Systems, 36, 2024.","title":"A novel approach for effective multi-view clustering with information-theoretic perspective","authors":"Chenhang Cui, Yazhou Ren, Jingyu Pu, Jiawei Li, Xiaorong Pu, Tianyi Wu, Yutao Shi, Lifang He","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Du et al., 2023] Yangfan Du, Gui-Fu Lu, and Guangyan Ji. Robust and optimal neighborhood graph learning for multi-view clustering. Information Sciences, 631:429- 448, 2023.","title":"Guangyan Ji. Robust and optimal neighborhood graph learning for multi-view clustering","authors":"Du","year":"2023","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fan et al., 2020] Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, and Bai Wang. One2multi graph au- toencoder for multi-view graph clustering. In WWW, pages 3070-3076, 2020.","title":"One2multi graph autoencoder for multi-view graph clustering","authors":"Xiao Shaohua Fan, Chuan Wang, Emiao Shi, Ken Lu, Bai Lin, Wang","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gönen and Margolin, 2014] Mehmet Gönen and Adam A Margolin. Localized data fusion for kernel k-means clus- tering with application to cancer biology. In NeurIPS, pages 1-9, 2014.","title":"Localized data fusion for kernel k-means clustering with application to cancer biology","authors":"Margolin ; ] Mehmet Gönen, Adam A Gönen, Margolin","year":"2014","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Han et al., 2022] Zongbo Han, Changqing Zhang, Huazhu Fu, and Joey Tianyi Zhou. Trusted multi-view classifica- tion with dynamic evidential fusion. TPAMI, 45(2):2551- 2566, 2022.","title":"Trusted multi-view classification with dynamic evidential fusion","authors":"Han","year":"2022","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hao et al., 2021] Wenyu Hao, Shanmin Pang, and Zhikai Chen. Multi-view spectral clustering via common struc- ture maximization of local and global representations. Neural Networks, 143:595-606, 2021.","title":"Multi-view spectral clustering via common structure maximization of local and global representations","authors":"Wenyu Hao, Shanmin Pang, Zhikai Chen","year":"2021","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hu et al., 2020] Shizhe Hu, Xiaoqiang Yan, and Yangdong Ye. Dynamic auto-weighted multi-view co-clustering. Pat- tern Recognition, 99:107101, 2020.","title":"Dynamic auto-weighted multi-view co-clustering","authors":"Hu","year":"2020","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kang et al., 2020] Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, and Zenglin Xu. Multi-graph fusion for multi-view spectral clustering. KBS, 189:105102, 2020.","title":"Multi-graph fusion for multi-view spectral clustering","authors":"Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, Zenglin Xu","year":"2020","venue":"KBS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2019a] Ruihuang Li, Changqing Zhang, Huazhu Fu, Xi Peng, Joey Tianyi Zhou, and Qinghua Hu. Recip- rocal multi-layer subspace learning for multi-view cluster- ing. In ICCV, pages 8172-8180, 2019.","title":"Reciprocal multi-layer subspace learning for multi-view clustering","authors":"Li","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019b] Zhaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, and Zhaohua Yang. Deep adversar- ial multi-view clustering network. In IJCAI, pages 2952- 2958, 2019.","title":"Deep adversarial multi-view clustering network","authors":"Li","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/409"},{"position":18,"raw":"[Li et al., 2020] Xuelong Li, Rui Zhang, Qi Wang, and Hongyuan Zhang. Autoencoder constrained clustering with adaptive neighbors. TNNLS, 32(1):443-449, 2020.","title":"Autoencoder constrained clustering with adaptive neighbors","authors":"Xuelong Li, Rui Zhang, Qi Wang, Hongyuan Zhang","year":"2020","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2021] Lusi Li, Zhiqiang Wan, and Haibo He. In- complete multi-view clustering with joint partition and graph learning. TKDE, pages 1-15, 2021.","title":"Incomplete multi-view clustering with joint partition and graph learning","authors":"Lusi Li, Zhiqiang Wan, Haibo He","year":"2021","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lin and Kang, 2022] Zhiping Lin and Zhao Kang. Graph filter-based multi-view attributed graph clustering. In IJ- CAI, pages 2723-2729, 2022.","title":"Graph filter-based multi-view attributed graph clustering","authors":"Zhiping Lin, Zhao Kang","year":"2022","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2021/375"},{"position":21,"raw":"[Lin et al., 2022] Fang-Yu Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, and Zenglin Xu. Contrastive multi-view hyperbolic hierarchical clustering. In IJCAI, 2022.","title":"Contrastive multi-view hyperbolic hierarchical clustering","authors":"Fang-Yu Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/451"},{"position":22,"raw":"[Lu et al., 2014] Yanting Lu, Liantao Wang, Jianfeng Lu, Jingyu Yang, and Chunhua Shen. Multiple kernel clus- tering based on centered kernel alignment. Pattern Recog- nition, 47:3656-3664, 2014.","title":"Multiple kernel clustering based on centered kernel alignment","authors":"Yanting Lu, Liantao Wang, Jianfeng Lu, Jingyu Yang, Chunhua Shen","year":"2014","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ma et al., 2022] Xuanlong Ma, Xueming Yan, Jingfa Liu, and Guo Zhong. Simultaneous multi-graph learning and clustering for multiview data. Information Sciences, 593:472-487, 2022.","title":"Simultaneous multi-graph learning and clustering for multiview data","authors":"Ma","year":"2022","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[MacQueen, 1967] James MacQueen. Classification and analysis of multivariate observations. In BSMSP, pages 281-297, 1967.","title":"Classification and analysis of multivariate observations","authors":"James Macqueen","year":"1967","venue":"BSMSP","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ng et al., 2001] Andrew Ng, Michael Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In NeurIPS, pages 1-8, 2001.","title":"On spectral clustering: Analysis and an algorithm","authors":"Andrew Ng, Michael Jordan, Yair Weiss","year":"2001","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Nie et al., 2020] Feiping Nie, Shaojun Shi, and Xuelong Li. Auto-weighted multi-view co-clustering via fast matrix factorization. Pattern Recognition, 102:107286, 2020.","title":"Auto-weighted multi-view co-clustering via fast matrix factorization","authors":"Nie","year":"2020","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Peng et al., 2019] Xi Peng, Hongyuan Zhu, Jiashi Feng, Chunhua Shen, Haixian Zhang, and Joey Tianyi Zhou. Deep clustering with sample-assignment invariance prior. TNNLS, 31(11):4857-4868, 2019.","title":"Deep clustering with sample-assignment invariance prior","authors":"Xi Peng, Hongyuan Zhu, Jiashi Feng, Chunhua Shen, Haixian Zhang, Joey Tianyi Zhou","year":"2019","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Peterson, 2009] Leif E Peterson. K-nearest neighbor. Schol- arpedia, 4(2):1883, 2009.","title":"K-nearest neighbor","authors":"Leif E Peterson","year":"2009","venue":"Scholarpedia","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ren et al., 2020] Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, and Zenglin Xu. Self-paced and auto-weighted multi-view clustering. Neurocomputing, 383:248-256, 2020.","title":"Self-paced and auto-weighted multi-view clustering","authors":"Shudong Yazhou Ren, Peng Huang, Minghao Zhao, Zenglin Han, Xu","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ren et al., 2022] Pengfei Ren, Haifeng Sun, Jiachang Hao, Jingyu Wang, Qi Qi, and Jianxin Liao. Mining multi- view information: A strong self-supervised framework for depth-based 3d hand pose and mesh estimation. In CVPR, pages 20555-20565, 2022.","title":"Mining multiview information: A strong self-supervised framework for depth-based 3d hand pose and mesh estimation","authors":"Haifeng Pengfei Ren, Jiachang Sun, Jingyu Hao, Qi Wang, Jianxin Qi, Liao","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ren et al., 2024] Yazhou Ren, Xinyue Chen, Jie Xu, Jingyu Pu, Yonghao Huang, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, and Lifang He. A novel federated multi- view clustering method for unaligned and incomplete data fusion. Information Fusion, 108:102357, 2024.","title":"A novel federated multiview clustering method for unaligned and incomplete data fusion","authors":"Xinyue Yazhou Ren, Jie Chen, Jingyu Xu, Yonghao Pu, Xiaorong Huang, Ce Pu, Xiaofeng Zhu, Zhifeng Zhu, Lifang Hao, He","year":"2024","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Rumelhart et al., 1986] David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. Nature, 323(6088):533-536, 1986.","title":"Learning representations by back-propagating errors","authors":"Geoffrey E David E Rumelhart, Ronald J Hinton, Williams","year":"1986","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Tang et al., 2020] Chang Tang, Xinwang Liu, Xinzhong Zhu, En Zhu, Zhigang Luo, Lizhe Wang, and Wen Gao. Cgd: Multi-view clustering via cross-view graph diffu- sion. In AAAI, pages 5924-5931, 2020.","title":"Cgd: Multi-view clustering via cross-view graph diffusion","authors":"Chang Tang, Xinwang Liu, Xinzhong Zhu, En Zhu, Zhigang Luo, Lizhe Wang, Wen Gao","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Trosten et al., 2021] Daniel J Trosten, Sigurd Lokse, Robert Jenssen, and Michael Kampffmeyer. Reconsidering repre- sentation alignment for multi-view clustering. In CVPR, pages 1255-1265, 2021.","title":"Reconsidering representation alignment for multi-view clustering","authors":"Sigurd Daniel J Trosten, Robert Lokse, Michael Jenssen, Kampffmeyer","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. JMLR, 9(11):2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2019] Hao Wang, Yan Yang, and Bing Liu. Gmc: Graph-based multi-view clustering. TKDE, 32(6):1116-1129, 2019.","title":"Gmc: Graph-based multi-view clustering","authors":"Hao Wang, Yan Yang, Bing Liu","year":"2019","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2021] Ru Wang, Lin Li, Xiaohui Tao, Xiao Dong, Peipei Wang, and Peiyu Liu. Trio-based collabora- tive multi-view graph clustering with multiple constraints. IPM, 58(3):102466, 2021.","title":"Trio-based collaborative multi-view graph clustering with multiple constraints","authors":"Ru Wang, Lin Li, Xiaohui Tao, Xiao Dong, Peipei Wang, Peiyu Liu","year":"2021","venue":"IPM","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2022a] Ru Wang, Lin Li, Xiaohui Tao, Peipei Wang, and Peiyu Liu. Contrastive and attentive graph learning for multi-view clustering. IPM, 59(4):102967, 2022.","title":"Contrastive and attentive graph learning for multi-view clustering","authors":"Wang","year":"2022","venue":"IPM","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2022b] Siwei Wang, Xinwang Liu, Li Liu, Wenxuan Tu, Xinzhong Zhu, Jiyuan Liu, Sihang Zhou, and En Zhu. Highly-efficient incomplete large-scale multi- view clustering with consensus bipartite graph. In CVPR, pages 9776-9785, 2022.","title":"Highly-efficient incomplete large-scale multiview clustering with consensus bipartite graph","authors":"Wang","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2023] Qianqian Wang, Zhiqiang Tao, Wei Xia, Quanxue Gao, Xiaochun Cao, and Licheng Jiao. Adver- sarial multiview clustering networks with adaptive fusion. TNNLS, 34:7635-7647, 2023.","title":"Adversarial multiview clustering networks with adaptive fusion","authors":"Qianqian Wang, Zhiqiang Tao, Wei Xia, Quanxue Gao, Xiaochun Cao, Licheng Jiao","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wen et al., 2024] Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, and Lifang He. Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering. In AAAI, pages 15841-15849, 2024.","title":"Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering","authors":"Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, Lifang He","year":"2024","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xia et al., 2021] Wei Xia, Qianqian Wang, Quanxue Gao, Xiangdong Zhang, and Xinbo Gao. Self-supervised graph convolutional network for multi-view clustering. TMM, pages 3182-3192, 2021.","title":"Self-supervised graph convolutional network for multi-view clustering","authors":"Wei Xia, Qianqian Wang, Quanxue Gao, Xiangdong Zhang, Xinbo Gao","year":"2021","venue":"TMM","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xiao et al., 2017] Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for bench- marking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","authors":"Han Xiao, Kashif Rasul, Roland Vollgraf","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Xie et al., 2016] Junyuan Xie, Ross Girshick, and Ali Farhadi. Unsupervised deep embedding for clustering analysis. In ICML, pages 478-487, 2016.","title":"Unsupervised deep embedding for clustering analysis","authors":"Junyuan Xie, Ross Girshick, Ali Farhadi","year":"2016","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Xu et al., 2021a] Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, and Zenglin Xu. Deep embedded multi-view clus- tering with collaborative training. Information Sciences, 573:279-290, 2021.","title":"Deep embedded multi-view clustering with collaborative training","authors":"Xu","year":"2021","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Xu et al., 2021b] Jie Xu, Yazhou Ren, Huayi Tang, Xi- aorong Pu, Xiaofeng Zhu, Ming Zeng, and Lifang He. Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clus- tering. In ICCV, pages 9234-9243, 2021.","title":"Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering","authors":"Xu","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Xu et al., 2023] Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu, S Yu Philip, and Lifang He. Self-supervised discriminative feature learn- ing for deep multi-view clustering. IEEE Transactions on Knowledge & Data Engineering, 35(07):7470-7482, 2023.","title":"Self-supervised discriminative feature learning for deep multi-view clustering","authors":"Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu, Yu Philip, Lifang He","year":"2023","venue":"IEEE Transactions on Knowledge & Data Engineering","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Yang et al., 2021] Lin Yang, Wentao Fan, and Nizar Bouguila. Deep clustering analysis via dual variational autoencoder with spherical latent embeddings. TNNLS, 2021.","title":"Deep clustering analysis via dual variational autoencoder with spherical latent embeddings","authors":"Lin Yang, Wentao Fan, Nizar Bouguila","year":"2021","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Yu et al., 2021] Xiao Yu, Hui Liu, Yan Wu, and Caiming Zhang. Fine-grained similarity fusion for multi-view spec- tral clustering. Information Sciences, 568:350-368, 2021.","title":"Fine-grained similarity fusion for multi-view spectral clustering","authors":"Xiao Yu, Hui Liu, Yan Wu, Caiming Zhang","year":"2021","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":50,"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":51,"raw":"[Zheng et al., 2020] Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, and Yaochen Li. Feature con- catenation multi-view subspace clustering. Neurocomput- ing, 379:89-102, 2020.","title":"Feature concatenation multi-view subspace clustering","authors":"Zheng","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zheng et al., 2021] Qinghai Zheng, Jihua Zhu, Yuanyuan Ma, Zhongyu Li, and Zhiqiang Tian. Multi-view subspace clustering networks with local and global graph informa- tion. Neurocomputing, 449:15-23, 2021.","title":"Multi-view subspace clustering networks with local and global graph information","authors":"Zheng","year":"2021","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhou and Shen, 2020] Runwu Zhou and Yi-Dong Shen. End-to-end adversarial-attention network for multi-modal clustering. In CVPR, pages 14619-14628, 2020.","title":"End-to-end adversarial-attention network for multi-modal clustering","authors":"Shen Zhou, Runwu Zhou, Yi-Dong Shen","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}