{"type":"ScholarlyArticle","title":"Partial Label Learning by Semantic Difference Maximization","authors":[{"name":"Lei Feng","slug":"lei-feng","given":"Lei","family":"Feng","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore; Alibaba-NTU Singapore Joint Research Institute , Singapore"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"Partial label learning is a weakly supervised learning framework, in which each instance is provided with multiple candidate labels while only one of them is correct. Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities. This paper gives a positive answer to this question for the first time. Specifically, if two instances do not share any common candidate labels, they cannot have the same ground-truth label. By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts.","doi":"10.24963/ijcai.2019/318","url":"https://ijcai.org/proceedings/2019/318","pdf":"https://ijcai.org/proceedings/2019/0318.pdf","markdown":"https://ijcai.org/proceedings/2019/318.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/318","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 A-L","track":"Main track","number":318,"pages":{"first":2294,"last":2300},"keywords":["Machine Learning: Classification","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Belkin et al., 2006] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled exam- ples. Journal of Machine Learning Research, 7(11):2399- 2434, 2006.","title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","authors":"Mikhail Belkin, Partha Niyogi, Vikas Sindhwani","year":"2006","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Briggs et al., 2012] Forrest Briggs, Xiaoli Z Fern, and Ra- viv Raich. Rank-loss support instance machines for miml instance annotation. In ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, pages 534-542, 2012.","title":"Rank-loss support instance machines for miml instance annotation","authors":"Forrest Briggs, Raviv Xiaoli Z Fern, Raich","year":"2012","venue":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2014] Yi-Chen Chen, Vishal M Patel, Rama Chellappa, and P Jonathon Phillips. Ambiguously labeled learning using dictionaries. IEEE Transactions on Infor- mation Forensics and Security, 9(12):2076-2088, 2014.","title":"Ambiguously labeled learning using dictionaries","authors":"Yi-Chen Chen, M Vishal, Rama Patel, Jonathon Chellappa, Phillips","year":"2014","venue":"IEEE Transactions on Information Forensics and Security","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2018] Ching-Hui Chen, Vishal M Patel, and Rama Chellappa. Learning from ambiguously labeled face images. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 40(7):1653-1667, 2018.","title":"Learning from ambiguously labeled face images","authors":"Ching-Hui Chen, M Vishal, Rama Patel, Chellappa","year":"2018","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cour et al., 2011] Timothee Cour, Ben Sapp, and Ben Taskar. Learning from partial labels. Journal of Machine Learning Research, 12(5):1501-1536, 2011.","title":"Learning from partial labels","authors":"Timothee Cour, Ben Sapp, Ben Taskar","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Feng and An, 2018] Lei Feng and Bo An. Leveraging la- tent label distributions for partial label learning. In Inter- national Joint Conference on Artificial Intelligence, pages 2107-2113, 2018.","title":"Leveraging latent label distributions for partial label learning","authors":"Lei Feng, Bo An","year":"2018","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/291"},{"position":7,"raw":"[Feng and An, 2019] Lei Feng and Bo An. Partial label learning with self-guided retraining. In AAAI Conference on Artificial Intelligence, 2019.","title":"Lei Feng and Bo An. Partial label learning with self-guided retraining","authors":"An Feng","year":"2019","venue":"AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gong et al., 2018] Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, and Dacheng Tao. A regulariza- tion approach for instance-based superset label learning. IEEE Transactions on Cybernetics, 48(3):967-978, 2018.","title":"A regularization approach for instance-based superset label learning","authors":"Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao","year":"2018","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Guillaumin et al., 2010] Matthieu Guillaumin, Jakob Ver- beek, and Cordelia Schmid. Multiple instance metric learning from automatically labeled bags of faces. Lecture Notes in Computer Science, 63(11):634-647, 2010.","title":"Multiple instance metric learning from automatically labeled bags of faces","authors":"Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid","year":"2010","venue":"Lecture Notes in Computer Science","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hüllermeier and Beringer, 2006] Eyke Hüllermeier and Jürgen Beringer. Learning from ambiguously labeled examples. Intelligent Data Analysis, 10(5):419-439, 2006.","title":"Eyke Hüllermeier and Jürgen Beringer. Learning from ambiguously labeled examples","authors":"Beringer Hüllermeier","year":"2006","venue":"Intelligent Data Analysis","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jin and Ghahramani, 2003] Rong Jin and Zoubin Ghahra- mani. Learning with multiple labels. In Neural Informa- tion Processing Systems, pages 921-928, 2003.","title":"Rong Jin and Zoubin Ghahramani","authors":"Jin, Ghahramani","year":"2003","venue":"Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu and Dietterich, 2012] Li-Ping Liu and Thomas G Diet- terich. A conditional multinomial mixture model for su- perset label learning. In Neural Information Processing Systems, pages 548-556, 2012.","title":"A conditional multinomial mixture model for superset label learning","authors":"Li-Ping Liu, Thomas G Dietterich","year":"2012","venue":"Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu and Dietterich, 2014] Li-Ping Liu and Thomas Diet- terich. Learnability of the superset label learning problem. In International Conference on Machine Learning, pages 1629-1637, 2014.","title":"Li-Ping Liu and Thomas Dietterich. Learnability of the superset label learning problem","authors":"Dietterich Liu","year":"2014","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Luo and Orabona, 2010] Jie Luo and Francesco Orabona. Learning from candidate labeling sets. In Neural Infor- mation Processing Systems, pages 1504-1512, 2010.","title":"Learning from candidate labeling sets","authors":"Orabona Luo, Jie Luo, Francesco Orabona","year":"2010","venue":"Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nguyen and Caruana, 2008] Nam Nguyen and Rich Carua- na. Classification with partial labels. In ACM SIGKD- D International Conference on Knowledge Discovery and Data Mining, pages 551-559, 2008.","title":"Nam Nguyen and Rich Caruana. Classification with partial labels","authors":"Caruana Nguyen","year":"2008","venue":"ACM SIGKD-D International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Panis and Lanitis, 2014] Gabriel Panis and Andreas Lanitis. An overview of research activities in facial age estimation using the fg-net aging database. In European Conference on Computer Vision, pages 737-750, 2014.","title":"An overview of research activities in facial age estimation using the fg-net aging database","authors":"Lanitis Panis, Gabriel Panis, Andreas Lanitis","year":"2014","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Schölkopf et al., 2002] Bernhard Schölkopf, Alexander J S- mola, et al. Learning with Kernels: Support Vector Ma- chines, Regularization, Optimization, and Beyond. MIT press, 2002.","title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","authors":"Bernhard Schölkopf, Alexander J Smola","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sriperumbudur and Lanckriet, 2009] Bharath K Sriperum- budur and Gert RG Lanckriet. On the convergence of the concave-convex procedure. In Neural Information Pro- cessing Systems, pages 1759-1767, 2009.","title":"On the convergence of the concave-convex procedure","authors":"K Bharath, Gert Rg Sriperumbudur, Lanckriet","year":"2009","venue":"Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sriperumbudur et al., 2011] Bharath K Sriperumbudur, David A Torres, and Gert RG Lanckriet. A majorization- minimization approach to the sparse generalized eigenval- ue problem. Machine Learning, 85(1-2):3-39, 2011.","title":"A majorizationminimization approach to the sparse generalized eigenvalue problem","authors":"David A Bharath K Sriperumbudur, Gert Rg Torres, Lanckriet","year":"2011","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tang and Zhang, 2017] Cai-Zhi Tang and Min-Ling Zhang. Confidence-rated discriminative partial label learning. In AAAI Conference on Artificial Intelligence, pages 2611- 2617, 2017.","title":"Confidence-rated discriminative partial label learning","authors":"Cai-Zhi Tang, Min-Ling Zhang","year":"2017","venue":"AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wu and Zhang, 2018] Xuan Wu and Min-Ling Zhang. To- wards enabling binary decomposition for partial label learning. In International Joint Conference on Artificial Intelligence, pages 2427-2436, 2018.","title":"Towards enabling binary decomposition for partial label learning","authors":"Zhang ; ] Wu, Xuan Wu, Min-Ling Zhang","year":"2018","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/398"},{"position":22,"raw":"[Yuille and Rangarajan, 2003] Alan L Yuille and Anand Rangarajan. The concave-convex procedure. Neural Com- putation, 15(4):915-936, 2003.","title":"The concave-convex procedure","authors":"Alan L Yuille, Anand Rangarajan","year":"2003","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zeng et al., 2013] Zi-Nan Zeng, Shi-Jie Xiao, Kui Jia, Tsung-Han Chan, Sheng-Hua Gao, Dong Xu, and Yi Ma. Learning by associating ambiguously labeled images. In IEEE Conference on Computer Vision and Pattern Recog- nition, pages 708-715, 2013.","title":"Learning by associating ambiguously labeled images","authors":"Zi-Nan Zeng, Shi-Jie Xiao, Kui Jia, Tsung-Han Chan, Sheng-Hua Gao, Dong Xu, Yi Ma","year":"2013","venue":"IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang and Yu, 2015] Min-Ling Zhang and Fei Yu. Solv- ing the partial label learning problem: An instance-based approach. In International Joint Conference on Artificial Intelligence, 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":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/568"},{"position":25,"raw":"[Zhang et al., 2016] Min-Ling Zhang, Bin-Bin Zhou, and Xu-Ying Liu. Partial label learning via feature-aware dis- ambiguation. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1335- 1344, 2016.","title":"Partial label learning via feature-aware disambiguation","authors":"Zhang","year":"2016","venue":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang et al., 2017] Min-Ling Zhang, Fei Yu, and Cai- Zhi Tang. Disambiguation-free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 29(10):2155-2167, 2017.","title":"Disambiguation-free partial label learning","authors":"Zhang","year":"2017","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}