{"type":"ScholarlyArticle","title":"CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network","authors":[{"name":"Jie Wen","slug":"jie-wen","given":"Jie","family":"Wen","affiliation":"Bio-Computing Research Center , Harbin Institute of Technology , Shenzhen , Shenzhen , China; Shenzhen Key Laboratory of Visual Object Detection and Recognition , Shenzhen , China; jiewen"},{"name":"Zheng Zhang","slug":"zheng-zhang","given":"Zheng","family":"Zhang","affiliation":"Bio-Computing Research Center , Harbin Institute of Technology , Shenzhen , Shenzhen , China; Shenzhen Key Laboratory of Visual Object Detection and Recognition , Shenzhen , China; Pengcheng Laboratory , Shenzhen , China"},{"name":"Yong Xu","slug":"yong-xu","given":"Yong","family":"Xu","affiliation":"Bio-Computing Research Center , Harbin Institute of Technology , Shenzhen , Shenzhen , China; Shenzhen Key Laboratory of Visual Object Detection and Recognition , Shenzhen , China; Pengcheng Laboratory , Shenzhen , China"},{"name":"Bob Zhang","slug":"bob-zhang","given":"Bob","family":"Zhang","affiliation":"Department of Computer and Information Science , University of Macau , Taipa , Macau , China"},{"name":"Lunke Fei","slug":"lunke-fei","given":"Lunke","family":"Fei","affiliation":"School of Computer Science and Technology , Guangdong University of Technology , Guangzhou , China"},{"name":"Guo-Sen Xie","slug":"guo-sen-xie","given":"Guo-Sen","family":"Xie","affiliation":"Inception Institute of Artificial Intelligence , Abu Dhabi , UAE"}],"abstract":"In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, \\emph{i.e.}, learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.","doi":"10.24963/ijcai.2020/447","url":"https://ijcai.org/proceedings/2020/447","pdf":"https://ijcai.org/proceedings/2020/0447.pdf","markdown":"https://ijcai.org/proceedings/2020/447.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/447","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":447,"pages":{"first":3230,"last":3236},"keywords":["Machine Learning: Deep Learning","Machine Learning: Dimensionality Reduction and Manifold Learning","Data Mining: Clustering, Unsupervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24420/file_24420.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[Asuncion and Newman, 2007] Arthur Asuncion and David Newman. Uci machine learning repository [http://archive.ics.uci.edu/ml], 2007.","title":"Arthur Asuncion and David Newman","authors":"Asuncion, Newman","year":"2007","venue":null,"doi":null,"url":"http://archive.ics.uci.edu/ml","internal_url":null},{"position":2,"raw":"[Cai et al., 2008] Deng Cai, Xiaofei He, Xiaoyun Wu, and Jiawei Han. Non-negative matrix factorization on mani- fold. In ICDM, pages 63-72, 2008.","title":"Non-negative matrix factorization on manifold","authors":"Deng Cai, Xiaofei He, Xiaoyun Wu, Jiawei Han","year":"2008","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":3,"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":4,"raw":"[Chao et al., 2017] Guoqing Chao, Shiliang Sun, and Jinbo Bi. A survey on multi-view clustering. arXiv preprint arXiv:1712.06246, 2017.","title":"A survey on multi-view clustering","authors":"Guoqing Chao, Shiliang Sun, Jinbo Bi","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Guo et al., 2017] Xifeng Guo, Long Gao, Xinwang Liu, and Jianping Yin. Improved deep embedded clustering with local structure preservation. In IJCAI, pages 1753-1759, 2017.","title":"Improved deep embedded clustering with local structure preservation","authors":"Xifeng Guo, Long Gao, Xinwang Liu, Jianping Yin","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/243"},{"position":6,"raw":"[Guo et al., 2018] Xifeng Guo, En Zhu, Xinwang Liu, and Jianping Yin. Deep embedded clustering with data aug- mentation. In ACML, pages 550-565, 2018.","title":"Deep embedded clustering with data augmentation","authors":"Xifeng Guo, En Zhu, Xinwang Liu, Jianping Yin","year":"2018","venue":"ACML","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Guo et al., 2019] Xifeng Guo, Xinwang Liu, En Zhu, X- inzhong Zhu, Miaomiao Li, Xin Xu, and Jianping Yin. Adaptive self-paced deep clustering with data augmenta- tion. IEEE TKDE, 2019.","title":"Adaptive self-paced deep clustering with data augmentation","authors":"Xifeng Guo, Xinwang Liu, En Zhu, Xinzhong Zhu, Miaomiao Li, Xin Xu, Jianping Yin","year":"2019","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":8,"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":9,"raw":"[Hu and Chen, 2019] Menglei Hu and Songcan Chen. One- pass incomplete multi-view clustering. In AAAI, pages 3838-3845, 2019.","title":"Menglei Hu and Songcan Chen. Onepass incomplete multi-view clustering","authors":"Chen Hu","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kang et al., 2017] Zhao Kang, Chong Peng, and Qiang Cheng. Clustering with adaptive manifold structure learn- ing. In ICDE, pages 79-82, 2017.","title":"Clustering with adaptive manifold structure learning","authors":"Zhao Kang, Chong Peng, Qiang Cheng","year":"2017","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[LeCun, 1998] Yann LeCun. The mnist database of hand- written digits. http://yann. lecun. com/exdb/mnist/, 1998.","title":"The mnist database of handwritten digits","authors":"Yann Lecun","year":null,"venue":null,"doi":null,"url":"http://yann.lecun.com/exdb/mnist/,1998","internal_url":null},{"position":12,"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":13,"raw":"[Liu et al., 2019] Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Ding- gang Shen, Jianping Yin, and Wen Gao. Multiple kernel k-means with incomplete kernels. IEEE TPAMI, 2019.","title":"Multiple kernel k-means with incomplete kernels","authors":"Liu","year":"2019","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Nie et al., 2019] Feiping Nie, Cheng-Long Wang, and Xue- long Li. K-multiple-means: A multiple-means clustering method with specified k clusters. In ACM SIGKDD, pages 959-967, 2019.","title":"K-multiple-means: A multiple-means clustering method with specified k clusters","authors":"Nie","year":"2019","venue":"ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rai et al., 2016] Nishant Rai, Sumit Negi, Santanu Chaud- hury, and Om Deshmukh. Partial multi-view clustering using graph regularized nmf. In ICPR, pages 2192-2197, 2016.","title":"Partial multi-view clustering using graph regularized nmf","authors":"Nishant Rai, Sumit Negi, Santanu Chaudhury, Om Deshmukh","year":"2016","venue":"ICPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shao et al., 2015] Weixiang Shao, Lifang He, and S Yu Philip. 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, S Yu, Philip","year":"2015","venue":"ECML PKDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shao et al., 2016] Weixiang Shao, Lifang He, Chun-ta Lu, and S Yu Philip. Online multi-view clustering with in- complete views. In ICBD, pages 1012-1017, 2016.","title":"Online multi-view clustering with incomplete views","authors":"Weixiang Shao, Lifang He, Chun-Ta Lu, S Yu, Philip","year":"2016","venue":"ICBD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Trivedi et al., 2010] Anusua Trivedi, Piyush Rai, Hal Daumé III, and Scott L DuVall. Multiview clustering with incomplete views. In NIPS Workshop, volume 224, pages 1-8, 2010.","title":"Multiview clustering with incomplete views","authors":"Trivedi","year":"2010","venue":"NIPS Workshop","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2018] Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu. Partial multi- view clustering via consistent GAN. In ICDM, pages 1290-1295, 2018.","title":"Partial multiview clustering via consistent GAN","authors":"Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, Yun Fu","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2019] Hao Wang, Linlin Zong, Bing Liu, Yan Yang, and Wei Zhou. Spectral perturbation meets incom- plete multi-view data. arXiv preprint arXiv:1906.00098, 2019.","title":"Spectral perturbation meets incomplete multi-view data","authors":"Hao Wang, Linlin Zong, Bing Liu, Yan Yang, Wei Zhou","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2019/510"},{"position":21,"raw":"[Wen et al., 2018a] Jie Wen, Na Han, Xiaozhao Fang, Lunke Fei, Ke Yan, and Shanhua Zhan. Low-rank preserving pro- jection via graph regularized reconstruction. IEEE TCYB, 49(4):1279-1291, 2018.","title":"Low-rank preserving projection via graph regularized reconstruction","authors":"Wen","year":"2018","venue":"IEEE TCYB","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wen et al., 2018b] Jie Wen, Zheng Zhang, Yong Xu, and Zuofeng Zhong. Incomplete multi-view clustering via graph regularized matrix factorization. In ECCV Work- shops, pages 593-608, 2018.","title":"Incomplete multi-view clustering via graph regularized matrix factorization","authors":"Wen","year":"2018","venue":"ECCV Workshops","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wen et al., 2019] Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, and Hong Liu. Unified embedding alignment with missing views inferring for incomplete multi-view clustering. In AAAI, volume 33, pages 5393- 5400, 2019.","title":"Unified embedding alignment with missing views inferring for incomplete multi-view clustering","authors":"Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Hong Liu","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wen et al., 2020] Jie Wen, Yong Xu, and Hong Liu. Incom- plete multiview spectral clustering with adaptive graph learning. IEEE TCYB, 50(4):1418-1429, 2020.","title":"Incomplete multiview spectral clustering with adaptive graph learning","authors":"Jie Wen, Yong Xu, Hong Liu","year":"2020","venue":"IEEE TCYB","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xiang et al., 2013] Shuo Xiang, Lei Yuan, Wei Fan, Yalin Wang, Paul M Thompson, and Jieping Ye. Multi-source learning with block-wise missing data for alzheimer's dis- ease prediction. In ACM SIGKDD, pages 185-193, 2013.","title":"Multi-source learning with block-wise missing data for alzheimer's disease prediction","authors":"Shuo Xiang, Lei Yuan, Wei Fan, Yalin Wang, Paul M Thompson, Jieping Ye","year":"2013","venue":"ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xie et al., 2016] Junyuan Xie, Ross Girshick, and Al- i 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":27,"raw":"[Yang et al., 2017] Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong. Towards k-means- friendly spaces: Simultaneous deep learning and clustering. In ICML, pages 3861-3870. JMLR, 2017.","title":"Towards k-meansfriendly spaces: Simultaneous deep learning and clustering","authors":"Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, Mingyi Hong","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2018] Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, and Ling Shao. Binary multi-view clus- tering. IEEE TPAMI, 41(7):1774-1782, 2018.","title":"Binary multi-view clustering","authors":"Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, Ling Shao","year":"2018","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":29,"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":30,"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."}