{"type":"ScholarlyArticle","title":"Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data","authors":[{"name":"Wenzhang Zhuge","slug":"wenzhang-zhuge","given":"Wenzhang","family":"Zhuge","affiliation":"College of Liberal Arts and Sciences , National University of Defense Technology , Changsha , China"},{"name":"Chenping Hou","slug":"chenping-hou","given":"Chenping","family":"Hou","affiliation":"College of Liberal Arts and Sciences , National University of Defense Technology , Changsha , China"},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":"School of Computer , National University of Defense Technology , Changsha , China"},{"name":"Hong Tao","slug":"hong-tao","given":"Hong","family":"Tao","affiliation":"College of Liberal Arts and Sciences , National University of Defense Technology , Changsha , China"},{"name":"Dongyun Yi","slug":"dongyun-yi","given":"Dongyun","family":"Yi","affiliation":"College of Liberal Arts and Sciences , National University of Defense Technology , Changsha , China"}],"abstract":"Incomplete multi-view clustering has attracted various attentions from diverse fields. Most existing methods factorize data to learn a unified representation linearly. Their performance may degrade when the relations between the unified representation and data of different views are nonlinear. Moreover, they need post-processing on the unified representations to extract the clustering indicators, which separates the consensus learning and subsequent clustering. To address these issues, in this paper, we propose a Simultaneous Representation Learning and Clustering (SRLC) method. Concretely, SRLC constructs similarity matrices to measure the relations between pair of instances, and learns low-dimensional representations of present instances on each view and a common probability label matrix simultaneously. Thus, the nonlinear information can be reflected by these representations and the clustering results can obtained from label matrix directly. An efficient iterative algorithm with guaranteed convergence is presented for optimization. Experiments on several datasets demonstrate the advantages of the proposed approach.","doi":"10.24963/ijcai.2019/623","url":"https://ijcai.org/proceedings/2019/623","pdf":"https://ijcai.org/proceedings/2019/0623.pdf","markdown":"https://ijcai.org/proceedings/2019/623.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/623","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":623,"pages":{"first":4482,"last":4488},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2018] Yang Cai, Yuanyuan Jiao, Wenzhang Zhuge, Tao Hong, and Chenping Hou. Partial multi-view spectral clustering. Neurocomputing, 311:316-324, 2018.","title":"Partial multi-view spectral clustering","authors":"Yang Cai, Yuanyuan Jiao, Wenzhang Zhuge, Tao Hong, Chenping Hou","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ding et al., 2005] Chris Ding, Xiaofeng He, and Horst D. Simon. Nonnegative lagrangian relaxation of k-means and spectral clustering. In ECML, pages 530-538, 2005.","title":"Nonnegative lagrangian relaxation of k-means and spectral clustering","authors":"Chris Ding, Xiaofeng He, Horst D Simon","year":"2005","venue":"ECML","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hastie et al., 2010] Trevor Hastie, Jerome Friedman, and Robert Tibshirani. The elements of statistical learning. Technometrics, 45(3):267-268, 2010.","title":"The elements of statistical learning","authors":"Trevor Hastie, Jerome Friedman, Robert Tibshirani","year":"2010","venue":"Technometrics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hou et al., 2010] Chenping Hou, Changshui Zhang, Yi Wu, and Feiping Nie. Multiple view semi-supervised dimen- sionality reduction. Pattern Recognition, 43(3):720-730, 2010.","title":"Multiple view semi-supervised dimensionality reduction","authors":"Hou","year":"2010","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Hu and Chen, 2018] Menglei Hu and Songcan Chen. Dou- bly aligned incomplete multi-view clustering. In IJCAI, pages 2262-2268, 2018.","title":"Doubly aligned incomplete multi-view clustering","authors":"Chen ; Hu, Menglei Hu, Songcan Chen","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/313"},{"position":6,"raw":"[Jing et al., 2017] Zhao Jing, Xijiong Xie, Xu Xin, and Shil- iang Sun. Multi-view learning overview: Recent progress and new challenges. Information Fusion, 38:43-54, 2017.","title":"Multi-view learning overview: Recent progress and new challenges","authors":"Zhao Jing, Xijiong Xie, Xu Xin, Shiliang Sun","year":"2017","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Karasuyama and Mamitsuka, 2013] Masayuki Karasuyama and Hiroshi Mamitsuka. Multiple graph label propagation by sparse integration. IEEE Transactions on Neural Net- works and Learning Systems, 24(12):1999-2012, 2013.","title":"Multiple graph label propagation by sparse integration","authors":"Masayuki Karasuyama, Hiroshi Mamitsuka","year":"2013","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2014] Shao-Yuan Li, Yuan Jiang, and Zhi-Hua Zhou. Partial multi-view clustering. In AAAI, pages 1968- 1974, 2014.","title":"Partial multi-view clustering","authors":"Shao-Yuan, Yuan Li, Zhi-Hua Jiang, Zhou","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Nie et al., 2016a] 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, 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":10,"raw":"[Nie et al., 2016b] 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":11,"raw":"[Nie et al., 2017] Feiping Nie, Rui Zhang, and Xuelong Li. A generalized power iteration method for solving quadratic problem on the stiefel manifold. Science China Information Sciences, 60(11):146-155, 2017.","title":"A generalized power iteration method for solving quadratic problem on the stiefel manifold","authors":"Nie","year":"2017","venue":"Science China Information Sciences","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Shao et al., 2015] Weixiang Shao, Lifang He, and Philip S. Yu. Multiple incomplete views clustering via weighted nonnegative matrix factorization with l 2,1 regularization. In ECML PKDD, pages 318-334, 2015.","title":"Multiple incomplete views clustering via weighted nonnegative matrix factorization with l 2,1 regularization","authors":"Weixiang Shao, Lifang He, Philip S Yu","year":"2015","venue":"ECML PKDD","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Sun, 2013] Shiliang Sun. A survey of multi-view ma- chine learning. Neural Computing and Applications, 23(7- 8):2031-2038, 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":14,"raw":"[Tao et al., 2017] Hong Tao, Chenping Hou, Feiping Nie, Jubo Zhu, and Dongyun Yi. Scalable multi-view semi- supervised classification via adaptive regression. IEEE Transactions on Image Processing, 26(9):4283-4296, 2017.","title":"Scalable multi-view semisupervised classification via adaptive regression","authors":"Tao","year":"2017","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tao et al., 2018] Hong Tao, Chenping Hou, Dongyun Yi, and Jubo Zhu. Unsupervised maximum margin incomplete multi-view clustering. In ICAI, pages 13-25, 2018.","title":"Unsupervised maximum margin incomplete multi-view clustering","authors":"Tao","year":"2018","venue":"ICAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Xu et al., 2015] Chang. Xu, Dacheng Tao, and Chao Xu. Multi-view learning with incomplete views. IEEE Trans- actions on Image Processing, 24(12):5812-5825, 2015.","title":"Multi-view learning with incomplete views","authors":"Xu","year":"2015","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Xu et al., 2016] Jinglin Xu, Junwei Han, and Feiping Nie. Discriminatively embedded k-means for multi-view clus- tering. In CVPR, pages 5356-5364, 2016.","title":"Discriminatively embedded k-means for multi-view clustering","authors":"Jinglin Xu, Junwei Han, Feiping Nie","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Yin et al., 2015] Qiyue Yin, Shu Wu, and Liang Wang. In- complete multi-view clustering via subspace learning. In CIKM, pages 383-392, 2015.","title":"Incomplete multi-view clustering via subspace learning","authors":"Qiyue Yin, Shu Wu, Liang Wang","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Yin et al., 2017] Qiyue Yin, Shu Wu, and Liang Wang. Uni- fied subspace learning for incomplete and unlabeled multi- view data. Pattern Recognition, 67(67):313-327, 2017.","title":"Unified subspace learning for incomplete and unlabeled multiview data","authors":"Qiyue Yin, Shu Wu, Liang Wang","year":"2017","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zelnik-Manor and Perona, 2004] Lihi Zelnik-Manor and Pietro Perona. Self-tuning spectral clustering. Advances in Neural Information Processing Systems, 17:1601-1608, 2004.","title":"Self-tuning spectral clustering","authors":"Zelnik-Manor, Perona ; Lihi Zelnik-Manor, Pietro Perona","year":"2004","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhao et al., 2016] Handong Zhao, Hongfu Liu, and Yun Fu. Incomplete multi-modal visual data grouping. In IJCAI, pages 2392-2398, 2016.","title":"Incomplete multi-modal visual data grouping","authors":"Handong Zhao, Hongfu Liu, Yun Fu","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/341"},{"position":22,"raw":"[Zhao et al., 2018] Liang Zhao, Zhikui Chen, Yi Yang, Z Jane Wang, and Victor CM Leung. Incomplete multi- view clustering via deep semantic mapping. Neurocom- puting, 275:1053-1062, 2018.","title":"Incomplete multiview clustering via deep semantic mapping","authors":"Liang Zhao, Zhikui Chen, Yi Yang, Jane Wang, Victor, Leung","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}