{"type":"ScholarlyArticle","title":"Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification","authors":[{"name":"Zhe Xue","slug":"zhe-xue","given":"Zhe","family":"Xue","affiliation":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia , School of Computer Science , Beijing University of Posts and Telecommunications , Beijing 100876 , China"},{"name":"Junping Du","slug":"junping-du","given":"Junping","family":"Du","affiliation":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia , School of Computer Science , Beijing University of Posts and Telecommunications , Beijing 100876 , China"},{"name":"Dawei Du","slug":"dawei-du","given":"Dawei","family":"Du","affiliation":"Computer Science Department , University at Albany , SUNY , Albany , NY , 12222 , USA"},{"name":"Wenqi Ren","slug":"wenqi-ren","given":"Wenqi","family":"Ren","affiliation":"State Key Laboratory of Information Security , Institute of Information Engineering , Chinese Academy of Sciences , Beijing 100093 , China"},{"name":"Siwei Lyu","slug":"siwei-lyu","given":"Siwei","family":"Lyu","affiliation":"Computer Science Department , University at Albany , SUNY , Albany , NY , 12222 , USA"}],"abstract":"Incomplete view information often results in failure cases of the conventional multi-view methods. To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification. Specifically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unified framework to jointly learn the deep correlated predictive subspace and multi-view shared and private label predictors. DCPSL is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classification. Extensive experimental results on various practical datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.","doi":"10.24963/ijcai.2019/559","url":"https://ijcai.org/proceedings/2019/559","pdf":"https://ijcai.org/proceedings/2019/0559.pdf","markdown":"https://ijcai.org/proceedings/2019/559.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/559","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":559,"pages":{"first":4026,"last":4032},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2010] Jian-Feng Cai, Emmanuel J Candès, and Zuowei Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956-1982, 2010.","title":"A singular value thresholding algorithm for matrix completion","authors":"Jian-Feng Cai, Emmanuel J Candès, Zuowei Shen","year":"2010","venue":"SIAM Journal on Optimization","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chua et al., 2009] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus- wide: a real-world web image database from national uni- versity of singapore. In Proceedings of the ACM interna- tional conference on image and video retrieval, page 48, 2009.","title":"Nuswide: a real-world web image database from national university of singapore","authors":"Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, Yantao Zheng","year":"2009","venue":"Proceedings of the ACM international conference on image and video retrieval","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Elhamifar and Vidal, 2013] Ehsan Elhamifar and Rene Vi- dal. Sparse subspace clustering: Algorithm, theory, and applications. TPAMI, 35(11):2765-2781, 2013.","title":"Sparse subspace clustering: Algorithm, theory, and applications","authors":"Ehsan Elhamifar, Rene Vidal","year":"2013","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Feiping Nie and Li, 2018] Jing Li Feiping Nie, Gguo- hao Cai and Xuelong Li. Auto-weighted multi-view learn- ing for image clustering and semi-supervised classifica- tion. TIP, 27(3):1501-1511, 2018.","title":"Auto-weighted multi-view learning for image clustering and semi-supervised classification","authors":"Jing Li, Feiping Nie, Gguohao Cai, Xuelong Li","year":"2018","venue":"TIP","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":"[Lange, 2008] Kenneth Lange. Coordinate descent algo- rithms for lasso penalized regression. Annals of Applied Statistics, 2(1):224-244, 2008.","title":"Coordinate descent algorithms for lasso penalized regression","authors":"Kenneth Lange","year":"2008","venue":"Annals of Applied Statistics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Li et al., 2007] Fei-Fei Li, Robert Fergus, and Pietro Peron- a. Learning generative visual models from few training ex- amples: An incremental bayesian approach tested on 101 object categories. CVIU, 106(1):59-70, 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":"CVIU","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":"[Lin et al., 2010] Zhouchen Lin, Minming Chen, and Y- i Ma. The augmented lagrange multiplier method for ex- act recovery of corrupted low-rank matrices. CoRR, ab- s/1009.5055, 2010.","title":"The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices","authors":"Lin","year":"2010","venue":"CoRR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2012] Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, and Huang Thomas S. Constrained nonnega- tive matrix factorization for image representation. TPAMI, 34(7):1299-1311, 2012.","title":"Constrained nonnegative matrix factorization for image representation","authors":"Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, Huang Thomas, S","year":"2012","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2013] Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. Robust recovery of subspace structures by low-rank representation. TPAMI, 35(1):171-184, 2013.","title":"Robust recovery of subspace structures by low-rank representation","authors":"Liu","year":"2013","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":12,"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, 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":13,"raw":"[Nilsback and Zisserman, 2006] Maria-Elena Nilsback and Andrew Zisserman. A visual vocabulary for flower classi- fication. In CVPR, pages 1447-1454, 2006.","title":"A visual vocabulary for flower classification","authors":"Maria-Elena Nilsback, Andrew Zisserman","year":"2006","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Shao et al., 2015] Weixiang Shao, Lifang He, and Philip 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 Yu","year":"2015","venue":"ECML PKDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tan et al., 2018] Qiaoyu Tan, Guoxian Yu, Carlotta Domeniconi, Jun Wang, and Zili Zhang. Incomplete multi-view weak-label learning. In IJCAI, pages 2703- 2709, 2018.","title":"Incomplete multi-view weak-label learning","authors":"Tan","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/375"},{"position":16,"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. TIP, 26(9):4283-4296, 2017.","title":"Scalable multi-view semisupervised classification via adaptive regression","authors":"Tao","year":"2017","venue":"TIP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Trigeorgis et al., 2014] George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, and Björn W Schuller. A deep semi-nmf model for learning hidden representations. In ICML, pages 1692-1700, 2014.","title":"A deep semi-nmf model for learning hidden representations","authors":"George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Björn W Schuller","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Vidal, 2011] René Vidal. Subspace clustering. SPM, 28(2):52-68, 2011.","title":"René Vidal. Subspace clustering","authors":"Vidal","year":"2011","venue":"SPM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wen et al., 2019] Jie Wen, Yong Xu, and Hong Liu. Incom- plete multiview spectral clustering with adaptive graph learning. TCYB, pages 1-12, 2019.","title":"Incomplete multiview spectral clustering with adaptive graph learning","authors":"Jie Wen, Yong Xu, Hong Liu","year":"2019","venue":"TCYB","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Xiao et al., 2016] Jianxiong Xiao, Krista A. Ehinger, James Hays, Antonio Torralba, and Aude Oliva. SUN database: Exploring a large collection of scene categories. IJCV, 119(1):3-22, 2016.","title":"SUN database: Exploring a large collection of scene categories","authors":"Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, Aude Oliva","year":"2016","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Xu et al., 2018] Nan Xu, Yanqing Guo, Xin Zheng, Qianyu Wang, and Xiangyang Luo. Partial multi-view subspace clustering. In ACM MM, pages 1794-1801, 2018.","title":"Partial multi-view subspace clustering","authors":"Nan Xu, Yanqing Guo, Xin Zheng, Qianyu Wang, Xiangyang Luo","year":"2018","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yang et al., 2013] Yi Yang, Jingkuan Song, Zi Huang, Zhi- gang Ma, Nicu Sebe, and Alexander G Hauptmann. Multi- feature fusion via hierarchical regression for multimedia analysis. TMM, 15(3):572-581, 2013.","title":"Multifeature fusion via hierarchical regression for multimedia analysis","authors":"Yi Yang, Jingkuan Song, Zi Huang, Zhigang Ma, Nicu Sebe, Alexander G Hauptmann","year":"2013","venue":"TMM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2018] Yang Yang, De-Chuan Zhan, Xiang- Rong Sheng, and Yuan Jiang. Semi-supervised multi- modal learning with incomplete modalities. In IJCAI, pages 2998-3004, 2018.","title":"Semi-supervised multimodal learning with incomplete modalities","authors":"Yang Yang, Xiang-Rong De-Chuan Zhan, Yuan Sheng, Jiang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang and Zhang, 2016] Lei Zhang and David Zhang. Vi- sual understanding via multi-feature shared learning with global consistency. TMM, 18(2):247-259, 2016.","title":"Visual understanding via multi-feature shared learning with global consistency","authors":"Lei Zhang, David Zhang","year":"2016","venue":"TMM","doi":null,"url":null,"internal_url":null},{"position":25,"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"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}