{"type":"ScholarlyArticle","title":"Tensor-based multi-view label enhancement for multi-label learning","authors":[{"name":"Fangwen Zhang","slug":"fangwen-zhang","given":"Fangwen","family":"Zhang","affiliation":"Key Laboratory of Information Perception and Systems for Public Security of MIIT , Nanjing University of Science and Technology , China; Jiangsu Key Lab of Image and Video Understanding for Social Security , Nanjing University of Science and Technology , China"},{"name":"Xiuyi Jia","slug":"xiuyi-jia","given":"Xiuyi","family":"Jia","affiliation":"Key Laboratory of Information Perception and Systems for Public Security of MIIT , Nanjing University of Science and Technology , China; Jiangsu Key Lab of Image and Video Understanding for Social Security , Nanjing University of Science and Technology , China; State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Weiwei Li","slug":"weiwei-li","given":"Weiwei","family":"Li","affiliation":"College of Astronautics , Nanjing University of Aeronautics and Astronautics , China"}],"abstract":"Label enhancement (LE) is a procedure of recovering the label distributions from the logical labels in the multi-label data, the purpose of which is to better represent and mine the label ambiguity problem through the form of label distribution. Existing LE work mainly concentrates on how to leverage the topological information of the feature space and the correlation among the labels, and all are based on single view data. In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE. Firstly, we introduce the tensor factorization to get the common subspace which contains the high-order relationships among different views. Secondly, we use the common representation and multiple views to jointly mine a more comprehensive topological structure in the dataset. Finally, the topological structure of the feature space is migrated to the label space to get the label distributions. Extensive comparative studies validate that the performance of multi-view multi-label learning can be improved significantly with TMV-LE.","doi":"10.24963/ijcai.2020/328","url":"https://ijcai.org/proceedings/2020/328","pdf":"https://ijcai.org/proceedings/2020/0328.pdf","markdown":"https://ijcai.org/proceedings/2020/328.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/328","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":328,"pages":{"first":2369,"last":2375},"keywords":["Machine Learning: Tensor and Matrix Methods","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24649/file_24649.m3u8","length_sec":262,"provider":"hls"}],"references":[{"position":1,"raw":"[Boutell et al., 2004] Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.","title":"Learning multi-label scene classification","authors":"Matthew R Boutell, Jiebo Luo, Xipeng Shen, Christopher M Brown","year":"2004","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boyd et al., 2011] Stephen P. Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed opti- mization and statistical learning via the alternating direc- tion method of multipliers. Foundations and Trends in Ma- chine Learning, 3(1):1-122, 2011.","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","authors":"Stephen P Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein","year":"2011","venue":"Foundations and Trends in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Duygulu et al., 2002] Pinar Duygulu, Kobus Barnard, João F. G. de Freitas, and David A. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed im- age vocabulary. In Proceeding of European Conference on Computer Vision, pages 97-112, 2002.","title":"Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary","authors":"Pinar Duygulu, Kobus Barnard, F G João, David A De Freitas, Forsyth","year":"2002","venue":"Proceeding of European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Elisseeff and Weston, 2001] André Elisseeff and Jason We- ston. A kernel method for multi-labelled classification. In Proceeding of Conference on Neural Information Process- ing Systems, pages 681-687, 2001.","title":"A kernel method for multi-labelled classification","authors":"Weston ; Elisseeff, André Elisseeff, Jason Weston","year":"2001","venue":"Proceeding of Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Everingham et al., 2010] Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and An- drew Zisserman. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2):303-338, 2010.","title":"Andrew Zisserman. The pascal visual object classes (VOC) challenge","authors":"Mark Everingham, Luc Van Gool, K I Christopher, John M Williams, Winn","year":"2010","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gayar et al., 2006] Neamat El Gayar, Friedhelm Schwenker, and Günther Palm. A study of the ro- bustness of KNN classifiers trained using soft labels. In Proceeding of Artificial Neural Networks in Pattern Recognition, pages 67-80, 2006.","title":"Neamat El Gayar, Friedhelm Schwenker, and Günther Palm. A study of the robustness of KNN classifiers trained using soft labels","authors":"Gayar","year":"2006","venue":"Proceeding of Artificial Neural Networks in Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Geng, 2016] Xin Geng. Label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 28(7):1734-1748, 2016.","title":"Label distribution learning","authors":"Xin Geng, Geng","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jia et al., 2019] Xiuyi Jia, Xiang Zheng, Weiwei Li, Changqing Zhang, and Zechao Li. Facial emotion distri- bution learning by exploiting low-rank label correlations locally. In Proceeding of Conference on Computer Vision and Pattern Recognition, pages 9841-9850, 2019.","title":"Facial emotion distribution learning by exploiting low-rank label correlations locally","authors":"Xiuyi Jia, Xiang Zheng, Weiwei Li, Changqing Zhang, Zechao Li","year":"2019","venue":"Proceeding of Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kolda and Bader, 2009] Tamara G. Kolda and Brett W. Bader. Tensor decompositions and applications. SIAM Review, 51(3):455-500, 2009.","title":"Tensor decompositions and applications","authors":"Tamara G Kolda, Brett W Bader","year":"2009","venue":"SIAM Review","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2015] Yu-Kun Li, Min-Ling Zhang, and Xin Geng. Leveraging implicit relative labeling-importance informa- tion for effective multi-label learning. In Proceeding of International Conference on Data Mining, pages 251-260, 2015.","title":"Leveraging implicit relative labeling-importance information for effective multi-label learning","authors":"Yu-Kun Li, Min-Ling Zhang, Xin Geng","year":"2015","venue":"Proceeding of International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2015] Meng Liu, Yong Luo, Dacheng Tao, Chao Xu, and Yonggang Wen. Low-rank multi-view learning in matrix completion for multi-label image classification. In Proceeding of AAAI Conference on Artificial Intelligence, pages 2778-2784, 2015.","title":"Low-rank multi-view learning in matrix completion for multi-label image classification","authors":"Meng Liu, Yong Luo, Dacheng Tao, Chao Xu, Yonggang Wen","year":"2015","venue":"Proceeding of AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Luo et al., 2013] Yong Luo, Dacheng Tao, Chang Xu, Chao Xu, Hong Liu, and Yonggang Wen. Multiview vector- valued manifold regularization for multilabel image clas- sification. IEEE Transactions on Neural Networks and Learning Systems, 24(5):709-722, 2013.","title":"Multiview vectorvalued manifold regularization for multilabel image classification","authors":"Yong Luo, Dacheng Tao, Chang Xu, Chao Xu, Hong Liu, Yonggang Wen","year":"2013","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Read et al., 2011] Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. Classifier chains for multi-label classification. Machine Learning, 85(3):333-359, 2011.","title":"Classifier chains for multi-label classification","authors":"Read","year":"2011","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Tibshirani, 1996] Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1):267-288, 1996.","title":"Regression shrinkage and selection via the lasso","authors":"Robert Tibshirani","year":"1996","venue":"Journal of the Royal Statistical Society","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tuia et al., 2011] Devis Tuia, Jochem Verrelst, Luis Alonso-Chorda, Fernando Pérez-Cruz, and Gustavo Camps-Valls. Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters, 8(4):804-808, 2011.","title":"Multioutput support vector regression for remote sensing biophysical parameter estimation","authors":"Devis Tuia, Jochem Verrelst, Luis Alonso-Chorda, Fernando Pérez-Cruz, Gustavo Camps-Valls","year":"2011","venue":"IEEE Geoscience and Remote Sensing Letters","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Xu et al., 2018] Ning Xu, An Tao, and Xin Geng. Label en- hancement for label distribution learning. In Proceeding of International Joint Conference on Artificial Intelligence, pages 2926-2932, 2018.","title":"Label enhancement for label distribution learning","authors":"Ning Xu, An Tao, Xin Geng","year":"2018","venue":"Proceeding of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/406"},{"position":17,"raw":"[Zhang and Zhou, 2007] Min-Ling Zhang and Zhi-Hua Zhou. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.","title":"ML-KNN: A lazy learning approach to multi-label learning","authors":"Zhou ; ] Min-Ling Zhang, Zhi-Hua Zhang, Zhou","year":"2007","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zhang and Zhou, 2014] Min-Ling Zhang and Zhi-Hua Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8):1819-1837, 2014.","title":"A review on multi-label learning algorithms","authors":"Zhou ; ] Min-Ling Zhang, Zhi-Hua Zhang, Zhou","year":"2014","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhang et al., 2013] Tongtao Zhang, Rongrong Ji, Wei Liu, Dacheng Tao, and Gang Hua. Semi-supervised learning with manifold fitted graphs. In Proceeding of International Joint Conference on Artificial Intelligence, pages 1896- 1902, 2013.","title":"Semi-supervised learning with manifold fitted graphs","authors":"Zhang","year":"2013","venue":"Proceeding of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2013/280"},{"position":20,"raw":"[Zhang et al., 2018] Changqing Zhang, Ziwei Yu, Qinghua Hu, Pengfei Zhu, Xinwang Liu, and Xiaobo Wang. Latent semantic aware multi-view multi-label classification. In Proceeding of AAAI Conference on Artificial Intelligence, pages 4414-4421, 2018.","title":"Latent semantic aware multi-view multi-label classification","authors":"Changqing Zhang, Ziwei Yu, Qinghua Hu, Pengfei Zhu, Xinwang Liu, Xiaobo Wang","year":"2018","venue":"Proceeding of AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhou et al., 2020] Tao Zhou, Changqing Zhang, Chen Gong, Harish Bhaskar, and Jie Yang. Multiview la- tent space learning with feature redundancy minimiza- tion. IEEE Transactions on Cybernetics, 50(4):1655- 1668, 2020.","title":"Multiview latent space learning with feature redundancy minimization","authors":"Tao Zhou, Changqing Zhang, Chen Gong, Harish Bhaskar, Jie Yang","year":"2020","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhu et al., 2016] Xiaofeng Zhu, Xuelong Li, and Shichao Zhang. Block-row sparse multiview multilabel learning for image classification. IEEE Transactions on Cybernet- ics, 46(2):450-461, 2016.","title":"Block-row sparse multiview multilabel learning for image classification","authors":"Xiaofeng Zhu, Xuelong Li, Shichao Zhang","year":"2016","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}