{"type":"ScholarlyArticle","title":"A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery","authors":[{"name":"Yijing Luo","slug":"yijing-luo","given":"Yijing","family":"Luo","affiliation":"PCA Lab , the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education , School of Computer Science and Engineering , Nanjing University of Science and Technology , China"},{"name":"Bo Han","slug":"bo-han","given":"Bo","family":"Han","affiliation":"Department of Computer Science , Hong Kong Baptist University , Hong Kong SAR , China"},{"name":"Chen Gong","slug":"chen-gong","given":"Chen","family":"Gong","affiliation":"PCA Lab , the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education , School of Computer Science and Engineering , Nanjing University of Science and Technology , China; Jiangsu Key Lab of Image and Video Understanding for Social Security; Department of Computing , Hong Kong Polytechnic University , Hong Kong SAR , China"}],"abstract":"Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery'' (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we utilize the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods.","doi":"10.24963/ijcai.2020/361","url":"https://ijcai.org/proceedings/2020/361","pdf":"https://ijcai.org/proceedings/2020/0361.pdf","markdown":"https://ijcai.org/proceedings/2020/361.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/361","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":361,"pages":{"first":2605,"last":2611},"keywords":["Machine Learning: Clustering","Machine Learning: Classification"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25346/file_25346.m3u8","length_sec":303,"provider":"hls"}],"references":[{"position":1,"raw":"[Bartels and Stewart, 1972] Richard H Bartels and George W Stew- art. Solution of the matrix equation ax+ xb= c. Communications of the ACM, 15(9):820-826, 1972.","title":"Solution of the matrix equation ax+ xb= c","authors":"H Richard, George W Bartels, Stewart","year":"1972","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bartlett and Mendelson, 2002] Peter L Bartlett and Shahar Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3(Nov):463-482, 2002.","title":"Rademacher and gaussian complexities: Risk bounds and structural results","authors":"L Peter, Shahar Bartlett, Mendelson","year":"2002","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Gao et al., 2016] Wei Gao, Lu Wang, Zhi-Hua Zhou, et al. Risk minimization in the presence of label noise. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.","title":"Risk minimization in the presence of label noise","authors":"Wei Gao, Lu Wang, Zhi-Hua Zhou","year":"2016","venue":"Thirtieth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gong et al., 2017] Chen Gong, Hengmin Zhang, Jian Yang, and Dacheng Tao. Learning with inadequate and incorrect supervi- sion. In 2017 IEEE International Conference on Data Mining (ICDM), pages 889-894. IEEE, 2017.","title":"Learning with inadequate and incorrect supervision","authors":"Chen Gong, Hengmin Zhang, Jian Yang, Dacheng Tao","year":"2017","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gong et al., 2019] Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, and Dacheng Tao. Loss decomposition and centroid estimation for positive and unlabeled learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.","title":"Loss decomposition and centroid estimation for positive and unlabeled learning","authors":"Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, Dacheng Tao","year":"2019","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grubbs, 1973] Frank E Grubbs. Errors of measurement, preci- sion, accuracy and the statistical comparison of measuring in- struments. Technometrics, 15(1):53-66, 1973.","title":"Errors of measurement, precision, accuracy and the statistical comparison of measuring instruments","authors":"E Frank, Grubbs","year":"1973","venue":"Technometrics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Han et al., 2018a] Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama. Masking: A new perspective of noisy supervision. In Advances in Neural Information Processing Systems, pages 5836-5846, 2018.","title":"Masking: A new perspective of noisy supervision","authors":"Han","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Han et al., 2018b] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co- teaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems, pages 8527-8537, 2018.","title":"Coteaching: Robust training of deep neural networks with extremely noisy labels","authors":"Han","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2015] Jin Huang, Feiping Nie, and Heng Huang. A new simplex sparse learning model to measure data similarity for clustering. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.","title":"A new simplex sparse learning model to measure data similarity for clustering","authors":"Jin Huang, Feiping Nie, Heng Huang","year":"2015","venue":"Twenty-Fourth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/502"},{"position":10,"raw":"[Jiang et al., 2017] Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li- Jia Li, and Li Fei-Fei. Mentornet: Learning data-driven curricu- lum for very deep neural networks on corrupted labels. arXiv preprint arXiv:1712.05055, 2017.","title":"Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels","authors":"Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Magoulas and Prentza, 1999] George D Magoulas and Andriana Prentza. Machine learning in medical applications. In Advanced Course on Artificial Intelligence, pages 300-307. Springer, 1999.","title":"Machine learning in medical applications","authors":"D George, Andriana Magoulas, Prentza","year":"1999","venue":"Advanced Course on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Miranda et al., 2009] André LB Miranda, Luís Paulo F Garcia, André CPLF Carvalho, and Ana C Lorena. Use of classification algorithms in noise detection and elimination. In International Conference on Hybrid Artificial Intelligence Systems, pages 417- 424. Springer, 2009.","title":"Use of classification algorithms in noise detection and elimination","authors":"Miranda","year":"2009","venue":"International Conference on Hybrid Artificial Intelligence Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Muhlenbach et al., 2004] Fabrice Muhlenbach, Stéphane Lallich, and Djamel A Zighed. Identifying and handling mislabelled instances. Journal of Intelligent Information Systems, 22(1):89- 109, 2004.","title":"Identifying and handling mislabelled instances","authors":"Fabrice Muhlenbach, Stéphane Lallich, Djamel A Zighed","year":"2004","venue":"Journal of Intelligent Information Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Natarajan et al., 2013] Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in Neural Information Processing Systems, pages 1196-1204, 2013.","title":"Learning with noisy labels","authors":"Nagarajan Natarajan, Pradeep K Inderjit S Dhillon, Ambuj Ravikumar, Tewari","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nie et al., 2016] Feiping Nie, Xiaoqian Wang, Michael I Jordan, and Heng Huang. The constrained laplacian rank algorithm for graph-based clustering. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.","title":"The constrained laplacian rank algorithm for graph-based clustering","authors":"Nie","year":"2016","venue":"Thirtieth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Northcutt et al., 2017] Curtis G Northcutt, Tailin Wu, and Isaac L Chuang. Learning with confident examples: Rank pruning for robust classification with noisy labels. arXiv preprint arXiv:1705.01936, 2017.","title":"Learning with confident examples: Rank pruning for robust classification with noisy labels","authors":"Tailin Curtis G Northcutt, Isaac L Wu, Chuang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Patriksson, 2008] Michael Patriksson. On the applicability and solution of bilevel optimization models in transportation science: A study on the existence, stability and computation of optimal solutions to stochastic mathematical programs with equilibrium constraints. Transportation Research Part B: Methodological, 42(10):843-860, 2008.","title":"On the applicability and solution of bilevel optimization models in transportation science: A study on the existence, stability and computation of optimal solutions to stochastic mathematical programs with equilibrium constraints","authors":"Michael Patriksson","year":"2008","venue":"Transportation Research Part B: Methodological","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Patrini et al., 2016] Giorgio Patrini, Frank Nielsen, Richard Nock, and Marcello Carioni. Loss factorization, weakly supervised learning and label noise robustness. In International Conference on Machine Learning, pages 708-717, 2016.","title":"Loss factorization, weakly supervised learning and label noise robustness","authors":"Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni","year":"2016","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Patrini et al., 2017] Giorgio Patrini, Alessandro Rozza, Aditya Kr- ishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1944-1952, 2017.","title":"Making deep neural networks robust to label noise: A loss correction approach","authors":"Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu","year":"2017","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shi et al., 2018] Hong Shi, Shaojun Pan, Jian Yang, and Chen Gong. Positive and unlabeled learning via loss decomposition and centroid estimation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pages 2689-2695, 2018.","title":"Positive and unlabeled learning via loss decomposition and centroid estimation","authors":"Shi","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/373"},{"position":21,"raw":"[Tanaka et al., 2018] Daiki Tanaka, Daiki Ikami, Toshihiko Ya- masaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5552-5560, 2018.","title":"Joint optimization framework for learning with noisy labels","authors":"Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2015] Zhangyang Wang, Yingzhen Yang, Shiyu Chang, Jinyan Li, Simon Fong, and Thomas S Huang. A joint optimization framework of sparse coding and discriminative clustering. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.","title":"A joint optimization framework of sparse coding and discriminative clustering","authors":"Wang","year":"2015","venue":"Twenty-Fourth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/552"},{"position":23,"raw":"[Wei et al., 2019] Yang Wei, Chen Gong, Shuo Chen, Tongliang Liu, Jian Yang, and Dacheng Tao. Harnessing side information for classification under label noise. IEEE Transactions on Neural Networks and Learning Systems, 2019.","title":"Harnessing side information for classification under label noise","authors":"Yang Wei, Chen Gong, Shuo Chen, Tongliang Liu, Jian Yang, Dacheng Tao","year":"2019","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xia et al., 2019] Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama. Are anchor points really indispensable in label-noise learning? Ad- vances in Neural Information Processing Systems, pages 6835- 6846, 2019.","title":"Are anchor points really indispensable in label-noise learning? Advances in Neural Information Processing Systems","authors":"Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xu et al., 2016] Chang Xu, Dacheng Tao, and Chao Xu. Robust extreme multi-label learning. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1275-1284. ACM, 2016.","title":"Robust extreme multi-label learning","authors":"Chang Xu, Dacheng Tao, Chao Xu","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}