{"type":"ScholarlyArticle","title":"Leveraging Latent Label Distributions for Partial Label Learning","authors":[{"name":"Lei Feng","slug":"lei-feng","given":"Lei","family":"Feng","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"In \r\npartial label learning, each training example is assigned a set of candidate\r\nlabels, only one of which is the ground-truth label. Existing partial label\r\nlearning frameworks either assume each candidate label of equal confidence or\r\nconsider the ground-truth label as a latent variable hidden in the\r\nindiscriminate candidate label set, while the different labeling confidence\r\nlevels of the candidate labels are regrettably ignored. In this paper, we\r\nformalize the different labeling confidence levels as the latent label\r\ndistributions, and propose a novel unified framework to estimate the latent\r\nlabel distributions while training the model simultaneously. Specifically, we\r\npresent a biconvex formulation with constrained local consistency and adopt an\r\nalternating method to solve this optimization problem. The process of\r\nalternating optimization exactly facilitates the mutual adaption of the model\r\ntraining and the constrained label propagation. Extensive experimental results\r\non controlled UCI datasets as well as real-world datasets clearly show the\r\neffectiveness of the proposed approach.","doi":"10.24963/ijcai.2018/291","url":"https://ijcai.org/proceedings/2018/291","pdf":"https://ijcai.org/proceedings/2018/0291.pdf","markdown":"https://ijcai.org/proceedings/2018/291.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/291","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":291,"pages":{"first":2107,"last":2113},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Machine Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"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":2,"raw":"[Chen et al., 2017] Ching-Hui Chen, Vishal M Patel, and Rama Chellappa. Learning from ambiguously labeled face images. arXiv preprint arXiv:1702.04455, 2017.","title":"Learning from ambiguously labeled face images","authors":"Ching-Hui Chen, M Vishal, Rama Patel, Chellappa","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cour et al., 2009] Timothee Cour, Benjamin Sapp, Chris Jordan, and Ben Taskar. Learning from ambiguously la- beled images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 919-926.","title":"Learning from ambiguously labeled images","authors":"Timothee Cour, Benjamin Sapp, Chris Jordan, Ben Taskar","year":null,"venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"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":5,"raw":"[Gong et al., 2017] 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, 2017.","title":"A regularization approach for instance-based superset label learning","authors":"Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, Dacheng Tao","year":"2017","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gorski et al., 2007] Jochen Gorski, Frank Pfeuffer, and Kathrin Klamroth. Biconvex sets and optimization with bi- convex functions: A survey and extensions. Mathematical Methods of Operations Research, 66(3):373-407, 2007.","title":"Biconvex sets and optimization with biconvex functions: A survey and extensions","authors":"Gorski","year":"2007","venue":"Mathematical Methods of Operations Research","doi":null,"url":null,"internal_url":null},{"position":7,"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":8,"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":9,"raw":"[Hüllermeier and Cheng, 2015] Eyke Hüllermeier and Wei- wei Cheng. Superset learning based on generalized loss minimization. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 260-275. Springer, 2015.","title":"Eyke Hüllermeier and Weiwei Cheng. Superset learning based on generalized loss minimization","authors":"Cheng Hüllermeier","year":"2015","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jin and Ghahramani, 2003] Rong Jin and Zoubin Ghahra- mani. Learning with multiple labels. In Advances in Neu- ral Information Processing Systems, pages 921-928, 2003.","title":"Rong Jin and Zoubin Ghahramani","authors":"Jin, Ghahramani","year":"2003","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu and Chang, 2009] Wei Liu and Shih-Fu Chang. Robust multi-class transductive learning with graphs. In Proceed- ings of IEEE Conference on Computer Vision and Pattern Recognition, pages 381-388, 2009.","title":"Robust multi-class transductive learning with graphs","authors":"Wei Liu, Shih-Fu Chang","year":"2009","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","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 Advances 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":"Advances in 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 Advances in Neural Information Processing Systems, pages 1504- 1512, 2010.","title":"Learning from candidate labeling sets","authors":"Orabona Luo, Jie Luo, Francesco Orabona","year":"2010","venue":"Advances in 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 Proceedings of the ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, pages 551-559. ACM, 2008.","title":"Nam Nguyen and Rich Caruana. Classification with partial labels","authors":"Caruana Nguyen","year":"2008","venue":"Proceedings of the ACM SIGKDD 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":"[Yu and Zhang, 2016] Fei Yu and Min-Ling Zhang. Max- imum margin partial label learning. In Proceedings of Asian Conference on Machine Learning, pages 96-111, 2016.","title":"Maximum margin partial label learning","authors":"Yu, Zhang ; ] Fei Yu, Min-Ling Zhang","year":"2016","venue":"Proceedings of Asian Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":18,"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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 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":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhang and Yu, 2015] Min-Ling Zhang and Fei Yu. Solv- ing the partial label learning problem: An instance-based approach. In Proceedings of the 24th International Join- t 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":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/568"},{"position":20,"raw":"[Zhang et al., 2016] Min-Ling Zhang, Bin-Bin Zhou, and Xu-Ying Liu. Partial label learning via feature-aware dis- ambiguation. In Proceedings of the ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, pages 1335-1344, 2016.","title":"Partial label learning via feature-aware disambiguation","authors":"Zhang","year":"2016","venue":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":21,"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},{"position":22,"raw":"[Zhou et al., 2004] Denny Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems, pages 321-328, 2004.","title":"Learning with local and global consistency","authors":"Denny Zhou, Olivier Bousquet, Jason Thomas N Lal, Bernhard Weston, Schölkopf","year":"2004","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhu and Goldberg, 2009] Xiao-Jin Zhu and Andrew B Goldberg. Introduction to semi-supervised learning. Syn- thesis lectures on artificial intelligence and machine learn- ing, 3(1):1-130, 2009.","title":"Introduction to semi-supervised learning","authors":"Xiao-Jin Zhu, Andrew B Goldberg","year":"2009","venue":"Synthesis lectures on artificial intelligence and machine learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}