{"type":"ScholarlyArticle","title":"Robust Auto-Weighted Multi-View Clustering","authors":[{"name":"Pengzhen Ren","slug":"pengzhen-ren","given":"Pengzhen","family":"Ren","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Yun Xiao","slug":"yun-xiao","given":"Yun","family":"Xiao","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Pengfei Xu","slug":"pengfei-xu","given":"Pengfei","family":"Xu","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Jun Guo","slug":"jun-guo","given":"Jun","family":"Guo","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Xiaojiang Chen","slug":"xiaojiang-chen","given":"Xiaojiang","family":"Chen","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"},{"name":"Dingyi Fang","slug":"dingyi-fang","given":"Dingyi","family":"Fang","affiliation":"School of Information Science and Technology , Northwest University , Xi'an 710127 , P.R. China"}],"abstract":"Multi-view clustering has played a vital role in real-world applications. It aims to cluster the data points into different groups by exploring complementary information of multi-view. A major challenge of this problem is how to learn the explicit cluster structure with multiple views when there is considerable noise. To solve this challenging problem, we propose a novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters. ℓ1-norm is employed for robustness of the proposed algorithm. We have validated this in the later experiment. The new graph learned by the proposed model approximates the original graphs of each individual view but maintains an explicit cluster structure. With this optimal graph, we can immediately achieve the clustering results without any further post-processing. We conduct extensive experiments to confirm the superiority and robustness of the proposed algorithm.","doi":"10.24963/ijcai.2018/367","url":"https://ijcai.org/proceedings/2018/367","pdf":"https://ijcai.org/proceedings/2018/0367.pdf","markdown":"https://ijcai.org/proceedings/2018/367.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/367","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":367,"pages":{"first":2644,"last":2650},"keywords":["Machine Learning: Clustering","Machine Learning: Machine Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Asuncion and Newman, 2007] Arthur Asuncion and David Newman. UCI machine learning repository, 2007.","title":"Arthur Asuncion and David Newman","authors":"Asuncion, Newman","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Blum and Mitchell, 1998] Avrim Blum and Tom M. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 92-100, 1998.","title":"Combining labeled and unlabeled data with co-training","authors":"Avrim Blum, Tom M Mitchell","year":"1998","venue":"Proceedings of the Eleventh Annual Conference on Computational Learning Theory","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2005] Deng Cai, Xiaofei He, and Jiawei Han. Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng., 17(12):1624-1637, 2005.","title":"Document clustering using locality preserving indexing","authors":"Deng Cai, Xiaofei He, Jiawei Han","year":"2005","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2011] Xiao Cai, Feiping Nie, Heng Huang, and Farhad Kamangar. Heterogeneous image feature integra- tion via multi-modal spectral clustering. In CVPR, pages 1977-1984, 2011.","title":"Heterogeneous image feature integration via multi-modal spectral clustering","authors":"Xiao Cai, Feiping Nie, Heng Huang, Farhad Kamangar","year":"2011","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chang et al., 2015] Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, and Xiaofang Zhou. A convex formulation for spectral shrunk clustering. In AAAI, pages 2532-2538, 2015.","title":"A convex formulation for spectral shrunk clustering","authors":"Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chang et al., 2016] Xiaojun Chang, Feiping Nie, Yi Yang, Chengqi Zhang, and Heng Huang. Convex sparse PCA for unsupervised feature learning. TKDD, 11(1):3:1-3:16, 2016.","title":"Convex sparse PCA for unsupervised feature learning","authors":"Xiaojun Chang, Feiping Nie, Yi Yang, Chengqi Zhang, Heng Huang","year":"2016","venue":"TKDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chao et al., 2017] Guoqing Chao, Shiliang Sun, and Jinbo Bi. A survey on multi-view clustering. Computing Re- search Repository, December 2017.","title":"A survey on multi-view clustering","authors":"Guoqing Chao, Shiliang Sun, Jinbo Bi","year":"2017","venue":"Computing Research Repository","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen and Dy, 2016] Junxiang Chen and Jennifer G. Dy. A generative block-diagonal model for clustering. In Pro- ceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence. Association for Uncertainty in Artificial Intelligence, 2016.","title":"A generative block-diagonal model for clustering","authors":"Chen, Dy ;, Junxiang Chen, Jennifer G Dy","year":"2016","venue":"Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Duchi et al., 2008] John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. Efficient projections onto the l 1 -ball for learning in high dimensions. In ICML, pages 272-279. ACM, 2008.","title":"Efficient projections onto the l 1 -ball for learning in high dimensions","authors":"John C Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra","year":"2008","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fan, 1949] Ky Fan. On a theorem of weyl concerning eigen- values of linear transformations ii. Proceedings of the Na- tional Academy of Sciences of the United States of Amer- ica, 35(11):652-655, January 1949.","title":"On a theorem of weyl concerning eigenvalues of linear transformations ii","authors":"Fan Ky","year":"1949","venue":"Proceedings of the National Academy of Sciences of the United States of America","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fan, 1997] R. K. Chung Fan. Spectral graph theory. Pub- lished for the Conference Board of the mathematical sci- ences by the American Mathematical Society, 1997.","title":"Spectral graph theory","authors":"R K Chung Fan","year":"1997","venue":"Published for the Conference Board of the mathematical sciences","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hagen and Kahng, 1992] Lars W. Hagen and Andrew B. Kahng. New spectral methods for ratio cut partitioning and clustering. IEEE Trans. on CAD of Integrated Circuits and Systems, 11(9):1074-1085, September 1992.","title":"New spectral methods for ratio cut partitioning and clustering","authors":"Kahng Hagen, Lars W Hagen, Andrew B Kahng","year":"1992","venue":"IEEE Trans. on CAD of Integrated Circuits and Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kang et al., 2017a] Zhao Kang, Chong Peng, and Qiang Cheng. Twin learning for similarity and clustering: A uni- fied kernel approach. 2017.","title":"Twin learning for similarity and clustering: A unified kernel approach","authors":"Kang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kang et al., 2017b] Zhao Kang, Chong Peng, Qiang Cheng, and Zenglin Xu. Unified spectral clustering with optimal graph. 2017. [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":"and Hal Daumé III. Co-regularized multi-view spectral clustering","authors":"Kang","year":"2011","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2007] Fei-Fei Li, Robert Fergus, and Pietro Per- ona. Learning generative visual models from few train- ing examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Un- derstanding, 106(1), 2007.","title":"Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories","authors":"Fei-Fei Li, Robert Fergus, Pietro Perona","year":"2007","venue":"Computer Vision and Image Understanding","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2015] Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang. Large-scale multi-view spectral cluster- ing via bipartite graph. In AAAI, pages 2750-2756, 2015.","title":"Large-scale multi-view spectral clustering via bipartite graph","authors":"Li","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Nie et al., 2014] Feiping Nie, Xiaoqian Wang, and Heng Huang. Clustering and projected clustering with adaptive neighbors. In ACM SIGKDD, pages 977-986, 2014.","title":"Clustering and projected clustering with adaptive neighbors","authors":"Nie","year":"2014","venue":"ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Nie et al., 2016] Feiping Nie, Xiaoqian Wang, Michael I. Jordan, and Heng Huang. The constrained laplacian rank algorithm for graph-based clustering. In AAAI, pages 1969-1976, 2016.","title":"The constrained laplacian rank algorithm for graph-based clustering","authors":"Nie","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Nie et al., 2017] Feiping Nie, Jing Li, and Xuelong Li. Self- weighted multiview clustering with multiple graphs. In IJCAI, pages 2564-2570, 2017.","title":"Selfweighted multiview clustering with multiple graphs","authors":"Nie","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pang et al., 2010] Yanwei Pang, Xuelong Li, and Yuan Yuan. Robust tensor analysis with l1-norm. IEEE Trans. Circuits Syst. Video Techn., 20(2):172-178, Febru- ary 2010.","title":"Robust tensor analysis with l1-norm","authors":"Yanwei Pang, Xuelong Li, Yuan Yuan","year":"2010","venue":"IEEE Trans. Circuits Syst. Video Techn","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sun, 2013] Shiliang Sun. A survey of multi-view ma- chine learning. Neural Computing and Applications, 23(7- 8):2031-2038, February 2013.","title":"A survey of multi-view machine learning","authors":"Shiliang Sun","year":"2013","venue":"Neural Computing and Applications","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tao et al., 2018] Hong Tao, Chenping Hou, Xinwang Liu, Dongyun Yi, and Jubo Zhu. Reliable multi-view cluster- ing. 2018.","title":"Reliable multi-view clustering","authors":"Tao","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[von Luxburg, 2007] Ulrike von Luxburg. A tutorial on spec- tral clustering. Statistics and Computing, 17(4):395-416, December 2007.","title":"A tutorial on spectral clustering","authors":"Ulrike Von, Luxburg","year":"2007","venue":"Statistics and Computing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Winn and Jojic, 2005] John M. Winn and Nebojsa Jojic. LOCUS: learning object classes with unsupervised seg- mentation. In ICCV, pages 756-763, 2005.","title":"LOCUS: learning object classes with unsupervised segmentation","authors":"John M Winn, Nebojsa Jojic","year":"2005","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":25,"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 AAAI, pages 2149-2155. AAAI, 2014.","title":"Robust multi-view spectral clustering via low-rank and sparse decomposition","authors":"Xia","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xu et al., 2013] Chang Xu, Dacheng Tao, and Chao Xu. A survey on multi-view learning. CoRR, April 2013.","title":"A survey on multi-view learning","authors":"Chang Xu, Dacheng Tao, Chao Xu","year":"2013","venue":"CoRR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Xue et al., 2017] Xiaowei Xue, Feiping Nie, Sen Wang, Xi- aojun Chang, Bela Stantic, and Min Yao. Multi-view cor- related feature learning by uncovering shared component. In AAAI, pages 2810-2816, 2017.","title":"Multi-view correlated feature learning by uncovering shared component","authors":"Xiaowei Xue, Feiping Nie, Sen Wang, Xiaojun Chang, Bela Stantic, Min Yao","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}