{"type":"ScholarlyArticle","title":"EMGC²F: Efficient Multi-view Graph Clustering with Comprehensive Fusion","authors":[{"name":"Danyang Wu","slug":"danyang-wu","given":"Danyang","family":"Wu","affiliation":"School of Computer Science , Northwestern Polytechnical University , Xi'an 710072 , P. R. China.; School of Artificial Intelligence, Optics and Electronics (iOPEN) , and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China."},{"name":"Jitao Lu","slug":"jitao-lu","given":"Jitao","family":"Lu","affiliation":"School of Computer Science , Northwestern Polytechnical University , Xi'an 710072 , P. R. China.; School of Artificial Intelligence, Optics and Electronics (iOPEN) , and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China."},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":"School of Computer Science , Northwestern Polytechnical University , Xi'an 710072 , P. R. China.; School of Artificial Intelligence, Optics and Electronics (iOPEN) , and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China."},{"name":"Rong Wang","slug":"rong-wang","given":"Rong","family":"Wang","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China."},{"name":"Yuan Yuan","slug":"yuan-yuan","given":"Yuan","family":"Yuan","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) , Northwestern Polytechnical University , Xi'an 710072 , P. R. China."}],"abstract":"This paper proposes an Efficient Multi-view Graph Clustering with Comprehensive Fusion (EMGC²F) model and a corresponding efficient optimization algorithm to address multi-view graph clustering tasks effectively and efficiently. Compared to existing works, our proposals have the following highlights: 1) EMGC²F directly finds a consistent cluster indicator matrix with a Super Nodes Similarity Minimization module from multiple views, which avoids time-consuming spectral decomposition in previous works. 2) EMGC²F comprehensively mines information from multiple views. More formally, it captures the consistency of multiple views via a Cross-view Nearest Neighbors Voting (CN²V) mechanism, meanwhile capturing the importance of multiple views via an adaptive weighted-learning mechanism. 3) EMGC²F is a parameter-free model and the time complexity of the proposed algorithm is far less than existing works, demonstrating the practicability. Empirical results on several benchmark datasets demonstrate that our proposals outperform SOTA competitors both in effectiveness and efficiency.","doi":"10.24963/ijcai.2022/495","url":"https://ijcai.org/proceedings/2022/495","pdf":"https://ijcai.org/proceedings/2022/0495.pdf","markdown":"https://ijcai.org/proceedings/2022/495.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/495","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":495,"pages":{"first":3566,"last":3572},"keywords":["Machine Learning: Clustering","Machine Learning: Multi-view learning","Machine Learning: Unsupervised Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986493","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984636","length_sec":320,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2003] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality re- duction and data representation. Neural Comput., 15(6):1373-1396, 2003.","title":"Laplacian eigenmaps for dimensionality reduction and data representation","authors":"Niyogi Belkin, Mikhail Belkin, Partha Niyogi","year":"2003","venue":"Neural Comput","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chao et al., 2021] Guoqing Chao, Shiliang Sun, and Jinbo Bi. A survey on multi-view clustering. IEEE Trans. on Artif. Intell., 2021.","title":"A survey on multi-view clustering","authors":"Guoqing Chao, Shiliang Sun, Jinbo Bi","year":"2021","venue":"IEEE Trans. on Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dong et al., 2014] Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, and Nikolai Nefedov. Clustering on multi- layer graphs via subspace analysis on grassmann mani- folds. IEEE Trans. Signal Process., 62(4):905-918, 2014.","title":"Clustering on multilayer graphs via subspace analysis on grassmann manifolds","authors":"Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai Nefedov","year":"2014","venue":"IEEE Trans. Signal Process","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dong et al., 2021] Xia Dong, Danyang Wu, Feiping Nie, Rong Wang, and Xuelong Li. Dependence-guided multi- view clustering. In ICASSP, pages 3650-3654. IEEE, 2021.","title":"Dependence-guided multiview clustering","authors":"Xia Dong, Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li","year":"2021","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Garling, 2005] D. J. H. Garling. The cauchy-schwarz master class: An introduction to the art of mathemati- cal inequalities by j. michael steele. Am. Math. Mon., 112(6):575-579, 2005.","title":"The cauchy-schwarz master class: An introduction to the art of mathematical inequalities by j. michael steele","authors":"D J H Garling","year":"2005","venue":"Am. Math. Mon","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hu et al., 2020] Zhanxuan Hu, Feiping Nie, Rong Wang, and Xuelong Li. Multi-view spectral clustering via inte- grating nonnegative embedding and spectral embedding. Inf. Fusion, 55:251-259, 2020.","title":"Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding","authors":"Hu","year":"2020","venue":"Inf. Fusion","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2021] Shudong Huang, Ivor Tsang, Zenglin Xu, and Jian Cheng Lv. Measuring diversity in graph learning: A unified framework for structured multi-view clustering. IEEE Trans. Knowl. Data Eng., 2021.","title":"Measuring diversity in graph learning: A unified framework for structured multi-view clustering","authors":"Huang","year":"2021","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kumar et al., 2011] Abhishek Kumar, Piyush Rai, and Hal Daumé III. Co-regularized multi-view spectral clus- tering. In NIPS, pages 1413-1421, 2011.","title":"Co-regularized multi-view spectral clustering","authors":"Abhishek Kumar, Piyush Rai, Hal Daumé, Iii","year":"2011","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lee and Grauman, 2009] Yong Jae Lee and Kristen Grau- man. Foreground focus: Unsupervised learning from par- tially matching images. Int. J. Comput. Vis., 85(2):143- 166, 2009.","title":"Foreground focus: Unsupervised learning from partially matching images","authors":"Jae Yong, Kristen Lee, Grauman","year":"2009","venue":"Int. J. Comput. Vis","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2020] Xuelong Li, Han Zhang, Rong Wang, and Feiping Nie. Multi-view clustering: A scalable and parameter-free bipartite graph fusion method. IEEE Trans. Pattern Anal. Mach. Intell., 2020.","title":"Multi-view clustering: A scalable and parameter-free bipartite graph fusion method","authors":"Xuelong Li, Han Zhang, Rong Wang, Feiping Nie","year":"2020","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2021] Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, and En Zhu. One pass late fusion multi-view clustering. In ICML, volume 139 of Proceedings of Machine Learn- ing Research, pages 6850-6859. PMLR, 2021.","title":"One pass late fusion multi-view clustering","authors":"Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, En Zhu","year":"2021","venue":"Proceedings of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"Nene et al., ] Sameer A Nene, Shree K Nayar, and Hiroshi Murase. Columbia object image library (coil-20).","title":"Columbia object image library","authors":"Nene","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Nie et al., 2016] Feiping Nie, Jing Li, and Xuelong Li. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In IJCAI, pages 1881-1887. IJCAI/AAAI Press, 2016.","title":"Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification","authors":"Nie","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/269"},{"position":14,"raw":"[Nie et al., 2017a] Feiping Nie, Guohao Cai, and Xuelong Li. Multi-view clustering and semi-supervised classifica- tion with adaptive neighbours. In AAAI, pages 2408-2414. AAAI Press, 2017.","title":"Multi-view clustering and semi-supervised classification with adaptive neighbours","authors":"Nie","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nie et al., 2017b] Feiping Nie, Jing Li, and Xuelong Li. Self-weighted multiview clustering with multiple graphs. In IJCAI, pages 2564-2570. ijcai.org, 2017.","title":"Self-weighted multiview clustering with multiple graphs","authors":"Nie","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/357"},{"position":16,"raw":"[Nie et al., 2018] Feiping Nie, Lai Tian, and Xuelong Li. Multiview clustering via adaptively weighted procrustes. In KDD, pages 2022-2030. ACM, 2018.","title":"Multiview clustering via adaptively weighted procrustes","authors":"Nie","year":"2018","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Reddy and Vinzamuri, 2013] Chandan K. Reddy and Bhanukiran Vinzamuri. A survey of partitional and hierarchical clustering algorithms. In Data Clustering: Algorithms and Applications, pages 87-110. CRC Press, 2013.","title":"A survey of partitional and hierarchical clustering algorithms","authors":"K Chandan, Bhanukiran Reddy, Vinzamuri","year":"2013","venue":"Data Clustering: Algorithms and Applications","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Samaria and Harter, 1994] Ferdinand Samaria and Andy Harter. Parameterisation of a stochastic model for human face identification. In WACV, pages 138-142. IEEE, 1994.","title":"Ferdinand Samaria and Andy Harter. Parameterisation of a stochastic model for human face identification","authors":"Harter Samaria","year":"1994","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tarjan, 1972] Robert Endre Tarjan. Depth-first search and linear graph algorithms. SIAM J. Comput., 1(2):146-160, 1972.","title":"Depth-first search and linear graph algorithms","authors":"Robert Endre, Tarjan","year":"1972","venue":"SIAM J. Comput","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wright, 2015] Stephen J. Wright. Coordinate descent algo- rithms. Math. Program., 151(1):3-34, 2015.","title":"Coordinate descent algorithms","authors":"Stephen J Wright","year":"2015","venue":"Math. Program","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wu et al., 2020] Danyang Wu, Feiping Nie, Rong Wang, and Xuelong Li. Multi-view clustering via mixed embed- ding approximation. In ICASSP, pages 3977-3981. IEEE, 2020.","title":"Multi-view clustering via mixed embedding approximation","authors":"Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li","year":"2020","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wu et al., 2021a] Danyang Wu, Zhanxuan Hu, Feiping Nie, Rong Wang, Hui Yang, and Xuelong Li. Multi-view clustering with interactive mechanism. Neurocomputing, 449:378-388, 2021.","title":"Multi-view clustering with interactive mechanism","authors":"Wu","year":"2021","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2021b] Danyang Wu, Feiping Nie, Xia Dong, Rong Wang, and Xuelong Li. Parameter-free consensus embedding learning for multiview graph-based clustering. IEEE Trans. Neural Netw. Learn. Syst., 2021.","title":"Parameter-free consensus embedding learning for multiview graph-based clustering","authors":"Wu","year":"2021","venue":"IEEE Trans. Neural Netw. Learn. Syst","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xu et al., 2013] Chang Xu, Dacheng Tao, and Chao Xu. A survey on multi-view learning. CoRR, abs/1304.5634, 2013.","title":"A survey on multi-view learning","authors":"Chang Xu, Dacheng Tao, Chao Xu","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhan et al., 2018] Kun Zhan, Changqing Zhang, Junpeng Guan, and Junsheng Wang. Graph learning for multi- view clustering. IEEE Trans. Cybern., 48(10):2887-2895, 2018.","title":"Graph learning for multiview clustering","authors":"Changqing Kun Zhan, Junpeng Zhang, Junsheng Guan, Wang","year":"2018","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}