{"type":"ScholarlyArticle","title":"Dynamic Hypergraph Structure Learning","authors":[{"name":"Zizhao Zhang","slug":"zizhao-zhang","given":"Zizhao","family":"Zhang","affiliation":"School of Software , Tsinghua University , China."},{"name":"Haojie Lin","slug":"haojie-lin","given":"Haojie","family":"Lin","affiliation":"School of Software , Tsinghua University , China."},{"name":"Yue Gao","slug":"yue-gao","given":"Yue","family":"Gao","affiliation":"School of Software , Tsinghua University , China."}],"abstract":"In recent years, hypergraph modeling has shown its superiority on correlation formulation among samples and has wide applications in classification, retrieval, and other tasks. In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure. A good hypergraph structure can represent the data correlation better, and vice versa. Although hypergraph learning has attracted much attention recently, most of existing works still rely on a static hypergraph structure, and little effort concentrates on optimizing the hypergraph structure during the learning process. To tackle this problem, we propose a dynamic hypergraph structure learning method in this paper. In this method, given the originally generated hypergraph structure, the objective of our work is to simultaneously optimize the label projection matrix (the common task in hypergraph learning) and the hypergraph structure itself. More specifically, in this formulation, the label projection matrix is related to the hypergraph structure, and the hypergraph structure is associated with the data correlation from both the label space and the feature space. Here, we alternatively learn the optimal label projection matrix and the hypergraph structure, leading to a dynamic hypergraph structure during the learning process. We have applied the proposed method in the tasks of 3D shape recognition and gesture recognition. Experimental results on 4 public datasets show better performance compared with the state-of-the-art methods. We note that the proposed method can be further applied in other tasks.","doi":"10.24963/ijcai.2018/439","url":"https://ijcai.org/proceedings/2018/439","pdf":"https://ijcai.org/proceedings/2018/0439.pdf","markdown":"https://ijcai.org/proceedings/2018/439.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/439","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":439,"pages":{"first":3162,"last":3169},"keywords":["Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Classification","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2017] Le An, Xiaojing Chen, Songfan Yang, and Xuelong Li. Person re-identification by multi-hypergraph fusion. IEEE Transactions on Neural Networks and Learn- ing Systems, 28(11):2763-2774, 2017.","title":"Person re-identification by multi-hypergraph fusion","authors":"Le An, Xiaojing Chen, Songfan Yang, Xuelong Li","year":"2017","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2003] Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung. On visual similarity based 3D model retrieval. Computer Graphics Forum, 22(3):223- 232, 2003.","title":"On visual similarity based 3D model retrieval","authors":"Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, Ming Ouhyoung","year":"2003","venue":"Computer Graphics Forum","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Du et al., 2017] Dawei Du, Honggang Qi, Longyin Wen, Qi Tian, Qingming Huang, and Siwei Lyu. Geometric hy- pergraph learning for visual tracking. IEEE Transactions on Cybernetics, 47(12):4182-4195, 2017.","title":"Geometric hypergraph learning for visual tracking","authors":"Dawei Du, Honggang Qi, Longyin Wen, Qi Tian, Qingming Huang, Siwei Lyu","year":"2017","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gao et al., 2012] Yue Gao, Meng Wang, Dacheng Tao, Rongrong Ji, and Qionghai Dai. 3-D object retrieval and recognition with hypergraph analysis. IEEE Transactions on Image Processing, 21(9):4290-4303, 2012.","title":"3-D object retrieval and recognition with hypergraph analysis","authors":"Yue Gao, Meng Wang, Dacheng Tao, Rongrong Ji, Qionghai Dai","year":"2012","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao et al., 2013] Yue Gao, Meng Wang, Zheng-Jun Zha, Jialie Shen, Xuelong Li, and Xindong Wu. Visual-textual joint relevance learning for tag-based social image search. IEEE Transactions on Image Processing, 22(1):363-376, 2013.","title":"Visual-textual joint relevance learning for tag-based social image search","authors":"Yue Gao, Meng Wang, Zheng-Jun Zha, Jialie Shen, Xuelong Li, Xindong Wu","year":"2013","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2009] Yuchi Huang, Qingshan Liu, and Dim- itris Metaxas. Video object segmentation by hypergraph cut. In Proceedings of IEEE Conference on Computer Vi- sion and Pattern Recognition, pages 1738-1745, 2009.","title":"Video object segmentation by hypergraph cut","authors":"Yuchi Huang, Qingshan Liu, Dimitris Metaxas","year":"2009","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2010] Yuchi Huang, Qingshan Liu, Shaoting Zhang, and Dimitris N Metaxas. Image retrieval via prob- abilistic hypergraph ranking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 3376-3383, 2010.","title":"Image retrieval via probabilistic hypergraph ranking","authors":"Yuchi Huang, Qingshan Liu, Shaoting Zhang, Dimitris N Metaxas","year":"2010","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hwang et al., 2008] TaeHyun Hwang, Ze Tian, Rui Kuangy, and Jean-Pierre Kocher. Learning on weighted hypergraphs to integrate protein interactions and gene ex- pressions for cancer outcome prediction. In Proceedings of IEEE International Conference on Data Mining, pages 293-302, 2008.","title":"Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction","authors":"Hwang","year":"2008","venue":"Proceedings of IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jayanti et al., 2006] Subramaniam Jayanti, Yagna- narayanan Kalyanaraman, Natraj Iyer, and Karthik Ramani. Developing an engineering shape benchmark for cad models. Computer-Aided Design, 38(9):939 -953, 2006.","title":"Developing an engineering shape benchmark for cad models","authors":"Subramaniam Jayanti, Yagnanarayanan Kalyanaraman, Natraj Iyer, Karthik Ramani","year":"2006","venue":"Computer-Aided Design","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li and Milenkovic, 2017] Pan Li and Olgica Milenkovic. Inhomogeneous hypergraph clustering with applications. In Proceedings of Advances in Neural Information Pro- cessing Systems, pages 2305-2315.","title":"Inhomogeneous hypergraph clustering with applications","authors":"Milenkovic ; Li, Pan Li, Olgica Milenkovic","year":"2017","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Curran Associates, Inc., 2017.","title":null,"authors":"Curran Associates, Inc","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2017a] Mingxia Liu, Jun Zhang, Pew-Thian Yap, and Dinggang Shen. View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi- modality data. Medical Image Analysis, 36:123-134, 2017.","title":"View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multimodality data","authors":"Liu","year":"2017","venue":"Medical Image Analysis","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2017b] Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, and Dacheng Tao. Elastic net hyper- graph learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, 26(1):452-463, 2017.","title":"Elastic net hypergraph learning for image clustering and semi-supervised classification","authors":"Liu","year":"2017","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Luo et al., 2018] Fulin Luo, Bo Du, Liangpei Zhang, Lefei Zhang, and Dacheng Tao. Feature learning using spatial- spectral hypergraph discriminant analysis for hyperspec- tral image. IEEE Transactions on Cybernetics, 2018.","title":"Feature learning using spatialspectral hypergraph discriminant analysis for hyperspectral image","authors":"Fulin Luo, Bo Du, Liangpei Zhang, Lefei Zhang, Dacheng Tao","year":"2018","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Oreifej and Liu, 2013] Omar Oreifej and Zicheng Liu. Hon4d: Histogram of oriented 4D normals for activity recognition from depth sequences. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 716-723, 2013.","title":"Hon4d: Histogram of oriented 4D normals for activity recognition from depth sequences","authors":"Liu Oreifej, Omar Oreifej, Zicheng Liu","year":"2013","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Su et al., 2015] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view con- volutional neural networks for 3D shape recognition. In Proceedings of IEEE International Conference on Com- puter Vision, 2015.","title":"Multi-view convolutional neural networks for 3D shape recognition","authors":"Su","year":"2015","venue":"Proceedings of IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2012] Jiang Wang, Zicheng Liu, Jan Chorowski, Zhuoyuan Chen, and Ying Wu. Robust 3D action recognition with random occupancy patterns. In Proceedings of European Conference on Computer Vision, pages 872-885, 2012.","title":"Robust 3D action recognition with random occupancy patterns","authors":"Jiang Wang, Zicheng Liu, Jan Chorowski, Zhuoyuan Chen, Ying Wu","year":"2012","venue":"Proceedings of European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zhang et al., 2017] Zhihong Zhang, Lu Bai, Yuanheng Liang, and Edwin Hancock. Joint hypergraph learning and sparse regression for feature selection. Pattern Recogni- tion, 63:291 -309, 2017.","title":"Joint hypergraph learning and sparse regression for feature selection","authors":"Zhihong Zhang, Lu Bai, Yuanheng Liang, Edwin Hancock","year":"2017","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhao et al., 2018] Wei Zhao, Shulong Tan, Ziyu Guan, Boxuan Zhang, Maoguo Gong, Zhengwen Cao, and Quan Wang. Learning to map social network users by unified manifold alignment on hypergraph. IEEE Transactions on Neural Networks and Learning Systems, 2018.","title":"Learning to map social network users by unified manifold alignment on hypergraph","authors":"Wei Zhao, Shulong Tan, Ziyu Guan, Boxuan Zhang, Maoguo Gong, Zhengwen Cao, Quan Wang","year":"2018","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zhou and Schölkopf, 2007] Jiayuan Huang Zhou, Denny and Bernhard Schölkopf. Learning with hypergraphs: Clustering, classification, and embedding. In Proceedings of Advances in Neural Information Processing Systems, pages 1601-1608. MIT Press, 2007.","title":"Learning with hypergraphs: Clustering, classification, and embedding","authors":"Schölkopf ; ] Jiayuan Huang Zhou, Denny Zhou, Bernhard Schölkopf","year":"2007","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhou et al., 2003] Denny Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. In Pro- ceedings of Advances in Neural Information Processing Systems, pages 321-328, 2003.","title":"Learning with local and global consistency","authors":"Denny Zhou, Olivier Bousquet, Jason Thomas N Lal, Bernhard Weston, Schölkopf","year":"2003","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhu et al., 2015] Lei Zhu, Jialie Shen, Hai Jin, Ran Zheng, and Liang Xie. Content-based visual landmark search via multimodal hypergraph learning. IEEE Transactions on Cybernetics, 45(12):2756-2769, 2015.","title":"Content-based visual landmark search via multimodal hypergraph learning","authors":"Lei Zhu, Jialie Shen, Hai Jin, Ran Zheng, Liang Xie","year":"2015","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhu et al., 2017a] Lei Zhu, Jialie Shen, Liang Xie, and Zhiyong Cheng. Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE transactions on cybernetics, 47(11):3941-3954, 2017.","title":"Unsupervised topic hypergraph hashing for efficient mobile image retrieval","authors":"Zhu","year":"2017","venue":"IEEE transactions on cybernetics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhu et al., 2017b] Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang, Rongyao Hu, and Wei He. Adaptive hypergraph learning for unsupervised feature selection. In Proceed- ings of International Joint Conference on Artificial Intelli- gence, pages 3581-3587, 2017.","title":"Adaptive hypergraph learning for unsupervised feature selection","authors":"Zhu","year":"2017","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/501"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}