{"type":"ScholarlyArticle","title":"Discovering Latent Class Labels for Multi-Label Learning","authors":[{"name":"Jun Huang","slug":"jun-huang","given":"Jun","family":"Huang","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo.; School of Computer Science and Technology , Anhui University of Technology ."},{"name":"Linchuan Xu","slug":"linchuan-xu","given":"Linchuan","family":"Xu","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo."},{"name":"Jing Wang","slug":"jing-wang","given":"Jing","family":"Wang","affiliation":"School of Computing and Mathematical Sciences , University of Greenwich."},{"name":"Lei Feng","slug":"lei-feng","given":"Lei","family":"Feng","affiliation":"School of Computer Science and Engineering , Nanyang Technological University."},{"name":"Kenji Yamanishi","slug":"kenji-yamanishi","given":"Kenji","family":"Yamanishi","affiliation":"Graduate School of Information Science and Technology , The University of Tokyo."}],"abstract":"Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets.\r\nDiscovering and exploring the latent labels hidden in the data may not only \r\nfind interesting knowledge but also help us to build a more robust learning model.\r\nIn this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously.\r\nExtensive experiments show a competitive performance of DLCL against other \r\nstate-of-the-art MLL approaches.","doi":"10.24963/ijcai.2020/423","url":"https://ijcai.org/proceedings/2020/423","pdf":"https://ijcai.org/proceedings/2020/0423.pdf","markdown":"https://ijcai.org/proceedings/2020/423.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/423","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":423,"pages":{"first":3058,"last":3064},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification","Data Mining: Classification, Semi-Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24446/file_24446.m3u8","length_sec":277,"provider":"hls"}],"references":[{"position":1,"raw":"[Beck and Teboulle, 2009] A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear in- verse problems. SIAM J. Imaging Sci., 2(1):183-202, 2009.","title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","authors":"A Beck, M Teboulle","year":"2009","venue":"SIAM J. Imaging Sci","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boutell et al., 2004] M. R. Boutell, J.-B. Luo, X.-P. Shen, and C. M. Brown. Learning multi-label scene classifica- tion. Pattern Recognit., 37(9):1757-1771, 2004.","title":"Learning multi-label scene classification","authors":"M R Boutell, J.-B Luo, X.-P Shen, C M Brown","year":"2004","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2019] C. Chen, H. Wang, W. Liu, X. Zhao, T. Hu, and G. Chen. Two-stage label embedding via neu- ral factorization machine for multi-label classification. In AAAI, pages 3304-3311, 2019.","title":"Two-stage label embedding via neural factorization machine for multi-label classification","authors":"C Chen, H Wang, W Liu, X Zhao, T Hu, G Chen","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Demšar, 2006] J. Demšar. Statistical comparisons of clas- sifiers over multiple data sets. JMLR, 7:1-30, 2006.","title":"Statistical comparisons of classifiers over multiple data sets","authors":"J Demšar","year":"2006","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gibaja and Ventura, 2015] E. Gibaja and S. Ventura. A tutorial on multilabel learning. ACM Comput. Surv., 47(3):52:1-52:38, 2015.","title":"A tutorial on multilabel learning","authors":"E Gibaja, S Ventura","year":"2015","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hu and Chen, 2019] M.-L. Hu and S.-C. Chen. One-pass incomplete multi-view clustering. In AAAI, pages 3838- 3845, 2019.","title":"One-pass incomplete multi-view clustering","authors":"M.-L Hu, S.-C Chen","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hua and Qi, 2008] X.-S. Hua and G.-J. Qi. Online multi- label active annotation: Towards large-scale content-based video search. In ACM MM, pages 141-150, 2008.","title":"Online multilabel active annotation: Towards large-scale content-based video search","authors":"X.-S Hua, G.-J Qi","year":"2008","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2016] J. Huang, G. Li, Q. Huang, and X. Wu. Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans. Knowl. Data Eng., 28(12):3309-3323, 2016.","title":"Learning label-specific features and class-dependent labels for multi-label classification","authors":"J Huang, G Li, Q Huang, X Wu","year":"2016","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2018] J. Huang, G. Li, Q. Huang, and X. Wu. Joint feature selection and classification for multilabel learning. IEEE Trans. Cybern., 48(3):876 -889, 2018.","title":"Joint feature selection and classification for multilabel learning","authors":"J Huang, G Li, Q Huang, X Wu","year":"2018","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2019] J. Huang, F. Qin, X. Zheng, Z. Cheng, Z. Yuan, W. Zhang, and Q. Huang. Improving multi-label classification with missing labels by learning label-specific features. Inf. Sci., 492:124-146, 2019.","title":"Improving multi-label classification with missing labels by learning label-specific features","authors":"J Huang, F Qin, X Zheng, Z Cheng, Z Yuan, W Zhang, Q Huang","year":"2019","venue":"Inf. Sci","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jain et al., 2019] H. Jain, V. Balasubramanian, B. Chunduri, and M. Varma. Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches. In WS- DM, pages 528-536, 2019.","title":"Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches","authors":"H Jain, V Balasubramanian, B Chunduri, M Varma","year":"2019","venue":"WS-DM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jia and Zhang, 2019] B.-B. Jia and M.-L. Zhang. Multi- dimensional classification via knn feature augmentation. In AAAI, pages 3975-3982, 2019.","title":"Multidimensional classification via knn feature augmentation","authors":"B.-B Jia, M.-L Zhang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kuzborskij et al., 2013] I. Kuzborskij, F. Orabona, and B. Caputo. From n to n+1: Multiclass transfer incremental learning. In CVPR, pages 3358-3365, 2013.","title":"From n to n+1: Multiclass transfer incremental learning","authors":"Kuzborskij","year":"2013","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Nguyen et al., 2016] V. Nguyen, T. D. Nguyen, T. Le, S. Venkatesh, and D. Phung. One-pass logistic regression for label-drift and large-scale classification on distributed systems. In ICDM, pages 1113-1118, 2016.","title":"One-pass logistic regression for label-drift and large-scale classification on distributed systems","authors":"V Nguyen, T D Nguyen, T Le, S Venkatesh, D Phung","year":"2016","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pham et al., 2015] A. Pham, R. Raich, X. Fern, and J. P. Ar- riaga. Multi-instance multi-label learning in the presence of novel class instances. In ICML, pages 2427-2435, 2015.","title":"Multi-instance multi-label learning in the presence of novel class instances","authors":"A Pham, R Raich, X Fern, J P Arriaga","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Tan et al., 2018] Q. Tan, G. Yu, C. Domeniconi, J. Wang, and Z. Zhang. Incomplete multi-view weak-label learning. In IJCAI, 2018.","title":"Incomplete multi-view weak-label learning","authors":"Q Tan, G Yu, C Domeniconi, J Wang, Z Zhang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/375"},{"position":17,"raw":"[Wang et al., 2019] H. Wang, W. Liu, Y. Zhao, C. Zhang, T. Hu, and G. Chen. Discriminative and correlative partial multi-label learning. In IJCAI, pages 3691-3697, 2019.","title":"Discriminative and correlative partial multi-label learning","authors":"H Wang, W Liu, Y Zhao, C Zhang, T Hu, G Chen","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/512"},{"position":18,"raw":"[Wei et al., 2019] T. Wei, W.-W. Tu, and Y.-F. Li. Learning for tail label data: A label-specific feature approach. In IJCAI, pages 3842-3848, 2019.","title":"Learning for tail label data: A label-specific feature approach","authors":"T Wei, W.-W Tu, Y.-F Li","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/533"},{"position":19,"raw":"[Wu et al., 2019] X. Wu, Q. Chen, Y. Hu, D. Wang, X. Chang, X. Wang, and M.-L. Zhang. Multi-view multi- label learning with view-specific information extraction. In IJCAI, pages 3884-3890, 2019.","title":"Multi-view multilabel learning with view-specific information extraction","authors":"X Wu, Q Chen, Y Hu, D Wang, X Chang, X Wang, M.-L Zhang","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Xing et al., 2018] Y. Xing, G. Yu, C. Domeniconi, J. Wang, and Z. Zhang. Multi-label co-training. In IJCAI, 2018.","title":"Multi-label co-training","authors":"Y Xing, G Yu, C Domeniconi, J Wang, Z Zhang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Xioufis et al., 2011] E. S. Xioufis, M. Spiliopoulou, G. T- soumakas, and I. Vlahavas. Dealing with concept drift and class imbalance in multi-label stream classification. In IJ- CAI, pages 1583-1588, 2011.","title":"Dealing with concept drift and class imbalance in multi-label stream classification","authors":"E S Xioufis, M Spiliopoulou, G Tsoumakas, I Vlahavas","year":"2011","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2011/266"},{"position":22,"raw":"[Xu et al., 2019] N. Xu, J. Lv, and X. Geng. Partial label learning via label enhancement. In AAAI, pages 3838- 3845, 2019.","title":"Partial label learning via label enhancement","authors":"N Xu, J Lv, X Geng","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2016] H. Yang, J. T. Zhou, and J. Cai. Improv- ing multi-label learning with missing labels by structured semantic correlations. In ECCV, pages 835-851, 2016.","title":"Improving multi-label learning with missing labels by structured semantic correlations","authors":"H Yang, J T Zhou, J Cai","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu et al., 2014] H. Yu, P. Jain, P. Kar, and I. S. Dhillon. Large-scale multi-label learning with missing labels. In ICML, pages 593-601, 2014.","title":"Large-scale multi-label learning with missing labels","authors":"H Yu, P Jain, P Kar, I S Dhillon","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang and Wu, 2015] M.-L. Zhang and L. Wu. Lift: Multi- label learning with label-specific features. IEEE Trans. Pattern Anal. Mach. Intell., 37(1):107-120, 2015.","title":"Lift: Multilabel learning with label-specific features","authors":"M.-L Zhang, L Wu","year":"2015","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang and Zhou, 2007] M.-L. Zhang and Z.-H. Zhou. Ml- knn: A lazy learning approach to multi-label learning. Pat- tern Recognit., 40(7):2038-2048, 2007.","title":"Mlknn: A lazy learning approach to multi-label learning","authors":"M.-L Zhang, Z.-H Zhou","year":"2007","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang and Zhou, 2014] M.-L. Zhang and Z.-H. Zhou. A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng., 26(8):1819-1837, 2014.","title":"A review on multi-label learning algorithms","authors":"M.-L Zhang, Z.-H Zhou","year":"2014","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2018] Y. Zhang, R. Henao, Z. Gan, Y. Li, and L. Carin. Multi-label learning from medical plain text with convolutional residual models. In MLHC, pages 280-294, 2018.","title":"Multi-label learning from medical plain text with convolutional residual models","authors":"Y Zhang, R Henao, Z Gan, Y Li, L Carin","year":"2018","venue":"MLHC","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2020] Y. Zhang, Y. Wang, X.-Y. Liu, S. Mi, and M.-L. Zhang. Large-scale multi-label classification using unknown streaming images. Pattern Recognit., page 107100, 2020.","title":"Large-scale multi-label classification using unknown streaming images","authors":"Y Zhang, Y Wang, X.-Y Liu, S Mi, M.-L Zhang","year":"2020","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhu et al., 2017a] Y. Zhu, K.-M. Ting, and Z.-H. Zhou. Dis- cover multiple novel labels in multi-instance multi-label learning. In AAAI, pages 2977-2983, 2017.","title":"Discover multiple novel labels in multi-instance multi-label learning","authors":"Zhu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhu et al., 2017b] Y. Zhu, K.-M. Ting, and Z.-H. Zhou. New class adaption via instance generation in one-pass class incremental learning. In ICDM, pages 1207-1212, 2017.","title":"New class adaption via instance generation in one-pass class incremental learning","authors":"Zhu","year":"2017","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhu et al., 2018] Y. Zhu, K.-M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. IEEE Trans. Knowl. Data Eng., 30(10):1901-1914, 2018.","title":"Multi-label learning with emerging new labels","authors":"Y Zhu, K.-M Ting, Z.-H Zhou","year":"2018","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}