{"type":"ScholarlyArticle","title":"Partial Multi-Label Learning via Multi-Subspace Representation","authors":[{"name":"Ziwei Li","slug":"ziwei-li","given":"Ziwei","family":"Li","affiliation":"Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology , Beijing Jiaotong University"},{"name":"Gengyu Lyu","slug":"gengyu-lyu","given":"Gengyu","family":"Lyu","affiliation":"Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology , Beijing Jiaotong University"},{"name":"Songhe Feng","slug":"songhe-feng","given":"Songhe","family":"Feng","affiliation":"Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology , Beijing Jiaotong University"}],"abstract":"Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the original label space into a latent label subspace and a label correlation matrix to reduce the negative effects of redundant labels, then we utilize the correlations among features to project the original noisy feature space to a feature subspace to resist the noisy feature information. Afterwards, we introduce a graph Laplacian regularization to constrain the label subspace to keep intrinsic structure among features and impose an orthogonality constraint on the correlations among features to guarantee discriminability of the feature subspace. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.","doi":"10.24963/ijcai.2020/362","url":"https://ijcai.org/proceedings/2020/362","pdf":"https://ijcai.org/proceedings/2020/0362.pdf","markdown":"https://ijcai.org/proceedings/2020/362.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/362","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":362,"pages":{"first":2612,"last":2618},"keywords":["Data Mining: Feature Extraction, Selection and Dimensionality Reduction","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24422/file_24422.m3u8","length_sec":291,"provider":"hls"}],"references":[{"position":1,"raw":"[Bertsekas, 1999] Dimitri P Bertsekas. Nonlinear program- ming. Athena scientific, 1999.","title":"Nonlinear programming","authors":"Dimitri P Bertsekas","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"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":"Jiebo Matthew R Boutell, Xipeng Luo, Christopher M Shen, Brown","year":"2004","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2015] Zheng Chen, Minmin Chen, Kilian Q. Weinberger, and Weixiong Zhang. Marginalized denois- ing for link prediction and multi-label learning. In AAAI, pages 1707-1713, 2015.","title":"Marginalized denoising for link prediction and multi-label learning","authors":"Zheng Chen, Minmin Chen, Kilian Q Weinberger, Weixiong Zhang","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Demšar, 2006] Janez Demšar. Statistical comparisons of classifiers over multiple data sets. JMLR, 7(1):1-30, 2006.","title":"Janez Demšar. Statistical comparisons of classifiers over multiple data sets","authors":"Demšar","year":"2006","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Diplaris et al., 2005] Sotiris Diplaris, Grigorios Tsoumakas, Pericles A Mitkas, and Ioannis Vlahavas. Protein classification with multiple algorithms. In PCI, volume 3746, pages 448-456, 2005.","title":"Protein classification with multiple algorithms","authors":"Sotiris Diplaris, Grigorios Tsoumakas, Pericles A Mitkas, Ioannis Vlahavas","year":"2005","venue":"PCI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Duygulu et al., 2002] Pinar Duygulu, Kobus Barnard, Joao FG de Freitas, and David A Forsyth. Object recogni- tion as machine translation: Learning a lexicon for a fixed image vocabulary. In ECCV, pages 97-112, 2002.","title":"Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary","authors":"Duygulu","year":"2002","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Elisseeff and Weston, 2001] André Elisseeff and Jason We- ston. A kernel method for multi-labelled classification. In NIPS, pages 681-687, 2001.","title":"A kernel method for multi-labelled classification","authors":"Weston ; Elisseeff, André Elisseeff, Jason Weston","year":"2001","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fang and Zhang, 2019] Jun-Peng Fang and Min-Ling Zhang. Partial multi-label learning via credible label elicitation. In AAAI, pages 3518-3525, 2019.","title":"Partial multi-label learning via credible label elicitation","authors":"Zhang ; Jun-Peng Fang, Min-Ling Fang, Zhang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Feng and An, 2019a] Lei Feng and Bo An. Partial label learning by semantic difference maxization. In IJCAI, pages 2294-2300, 2019.","title":"Partial label learning by semantic difference maxization","authors":"An ; Feng, Lei Feng, Bo An","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Feng and An, 2019b] Lei Feng and Bo An. Partial label learning with self-guided retraining. In AAAI, pages 3542- 3549, 2019.","title":"Partial label learning with self-guided retraining","authors":"An ; Feng, Lei Feng, Bo An","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Feng et al., 2019] Lei Feng, Bo An, and Shuo He. Collab- oration based multi-label learning. In AAAI, pages 3550- 3557, 2019.","title":"Collaboration based multi-label learning","authors":"Lei Feng, Bo An, Shuo He","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gibaja and Ventura, 2015] Eva Gibaja and Sebastian Ven- tura. A tutorial on multi-label learning. CSUR, 47(3):1-38, 2015.","title":"Eva Gibaja and Sebastian Ventura. A tutorial on multi-label learning","authors":"Ventura Gibaja","year":"2015","venue":"CSUR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jin and Ghahramani, 2003] Rong Jin and Zoubin Ghahra- mani. Learning with multiple labels. In NIPS, pages 921- 928, 2003.","title":"Rong Jin and Zoubin Ghahramani","authors":"Jin, Ghahramani","year":"2003","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Katakis et al., 2008] Ioannis Katakis, Grigorios Tsoumakas, and Ioannis Vlahavas. Multilabel text classi- fication for automated tag suggestion. In ECML/PKDD, pages 5-13, 2008.","title":"Multilabel text classification for automated tag suggestion","authors":"Ioannis Katakis, Grigorios Tsoumakas, Ioannis Vlahavas","year":"2008","venue":"ECML/PKDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu and Tsang, 2017] Weiwei Liu and Ivor Tsang. Making decision trees feasible in ultrahigh feature and label di- mensions. JMLR, 18:1-36, 07 2017.","title":"Making decision trees feasible in ultrahigh feature and label dimensions","authors":"Tsang Liu, Weiwei Liu, Ivor Tsang","year":"2017","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2018] Weiwei Liu, Donna Xu, Ivor W Tsang, and Wenjie Zhang. Metric learning for multi-output tasks. TPAMI, 41(2):408-422, 2018.","title":"Metric learning for multi-output tasks","authors":"Weiwei Liu, Donna Xu, Ivor W Tsang, Wenjie Zhang","year":"2018","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lyu et al., 2019] Gengyu Lyu, Songhe Feng, Tao Wang, Congyan Lang, and Yidong Li. Gm-pll: Graph matching based partial label learning. TKDE, pages 1-15, 2019.","title":"Gm-pll: Graph matching based partial label learning","authors":"Lyu","year":"2019","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lyu et al., 2020] Gengyu Lyu, Songhe Feng, Yidong Li, Yi Jin, Guojun Dai, and Congyan Lang. Hera: Partial label learning by combining heterogeneous loss with sparse and low-rank regularization. TIST, 11(3):1-19, 2020.","title":"Hera: Partial label learning by combining heterogeneous loss with sparse and low-rank regularization","authors":"Lyu","year":"2020","venue":"TIST","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mencía and Fürnkranz, 2008] Eneldo Mencía and Johannes Fürnkranz. Efficient pairwise multilabel classification for large-scale problems in the legal domain. In ECML/PKDD, pages 50-65, 2008.","title":"Eneldo Mencía and Johannes Fürnkranz. Efficient pairwise multilabel classification for large-scale problems in the legal domain","authors":"Fürnkranz Mencía","year":"2008","venue":"ECML/PKDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pestian et al., 2007] John Pestian, Chris Brew, Paweł Matykiewicz, D. Hovermale, Neil Johnson, Kevin Cohen, and Duch Wlodzislaw. A shared task involving multi-label classification of clinical free text. In BioNLP '07, pages 97-104, 2007.","title":"A shared task involving multi-label classification of clinical free text","authors":"John Pestian, Chris Brew, D Paweł Matykiewicz, Neil Hovermale, Kevin Johnson, Duch Cohen, Wlodzislaw","year":"2007","venue":"BioNLP '07","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sun et al., 2010] Yu-Yin Sun, Yin Zhang, and Zhi-Hua Zhou. Multi-label learning with weak label. In AAAI, pages 593-598, 2010.","title":"Multi-label learning with weak label","authors":"Yu-Yin Sun, Yin Zhang, Zhi-Hua Zhou","year":"2010","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sun et al., 2019] Lijuan Sun, Songhe Feng, Tao Wang, Con- gyan Lang, and Yi Jin. Partial multi-label learning by low-rank and sparse decomposition. In AAAI, pages 5016- 5023, 2019.","title":"Partial multi-label learning by low-rank and sparse decomposition","authors":"Lijuan Sun, Songhe Feng, Tao Wang, Congyan Lang, Yi Jin","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Trohidis et al., 2008] Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, and Ioannis P Vlahavas. Multi-label classification of music into emotions. In ISMIR, pages 325-330, 2008.","title":"Multi-label classification of music into emotions","authors":"Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, Ioannis P Vlahavas","year":"2008","venue":"ISMIR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2019] Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu, and Gang Chen. Discrimina- tive and correlative partial multi-label learning. In IJCAI, pages 3691-3697, 2019.","title":"Discriminative and correlative partial multi-label learning","authors":"Wang","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/512"},{"position":25,"raw":"[Wu and Zhang, 2018] Xuan Wu and Min-Ling Zhang. To- wards enabling binary decomposition for partial label learning. In IJCAI, pages 2868-2874, 2018.","title":"Towards enabling binary decomposition for partial label learning","authors":"Zhang ; ] Wu, Xuan Wu, Min-Ling Zhang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/398"},{"position":26,"raw":"[Xie and Huang, 2018] Ming-Kun Xie and Sheng-Jun Huang. Partial multi-label learning. In AAAI, pages 4302-4309, 2018.","title":"Partial multi-label learning","authors":"Huang Xie, Ming-Kun Xie, Sheng-Jun Huang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yu et al., 2018] Guo-Xian Yu, Xia Chen, Carlotta Domeni- coni, Jun Wang, Zhao Li, Zili Zhang, and Xindong Wu. Feature-induced partial multi-label learning. In ICDM, pages 1398-1403, 2018.","title":"Feature-induced partial multi-label learning","authors":"Guo-Xian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang, Zhao Li, Zili Zhang, Xindong Wu","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang and Yu, 2015] Min-Ling Zhang and Fei Yu. Solving the partial label learning problem: An instance-based ap- proach. In IJCAI, pages 4048-4054, 2015.","title":"Solving the partial label learning problem: An instance-based approach","authors":"Yu ; ] Min-Ling Zhang, Fei Zhang, Yu","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/568"},{"position":29,"raw":"[Zhang and Zhou, 2007] Min-Ling Zhang and Zhi-Hua Zhou. Ml-knn: A lazy learning approach to multi-label leaming. Pattern Recognition, 40(7):2038-2048, 2007.","title":"Ml-knn: A lazy learning approach to multi-label leaming","authors":"Zhou ; ] Min-Ling Zhang, Zhi-Hua Zhang, Zhou","year":"2007","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhang et al., 2017] Min-Ling Zhang, Fei Yu, and Cai-Zhi Tang. Disambiguation-free partial label learning. TKDE, 29(10):2155-2167, 2017.","title":"Disambiguation-free partial label learning","authors":"Zhang","year":"2017","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhu et al., 2017] Yue Zhu, James T Kwok, and Zhi-Hua Zhou. Multi-label learning with global and local label cor- relation. TKDE, 30(6):1081-1094, 2017.","title":"Multi-label learning with global and local label correlation","authors":"Yue Zhu, James T Kwok, Zhi-Hua Zhou","year":"2017","venue":"TKDE","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}