{"type":"ScholarlyArticle","title":"Label Enhancement for Label Distribution Learning","authors":[{"name":"Ning Xu","slug":"ning-xu","given":"Ning","family":"Xu","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China; School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"An Tao","slug":"an-tao","given":"An","family":"Tao","affiliation":"School of Information Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"Xin Geng","slug":"xin-geng","given":"Xin","family":"Geng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China; School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"}],"abstract":"Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly.  To solve the problem, one way is to recover the label distributions from the logical labels in the training set via  leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world datasets show clear advantages of GLLE over several existing LE algorithms.","doi":"10.24963/ijcai.2018/406","url":"https://ijcai.org/proceedings/2018/406","pdf":"https://ijcai.org/proceedings/2018/0406.pdf","markdown":"https://ijcai.org/proceedings/2018/406.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/406","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":406,"pages":{"first":2926,"last":2932},"keywords":["Machine Learning: Machine Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Cabral et al., 2011] Ricardo S. Cabral, Fernando De la Torre, João P. Costeira, and Alexandre Bernardino. Ma- trix completion for multi-label image classification. In Ad- vances in Neural Information Processing Systems, pages 190-198, Granada SPAIN, 2011.","title":"Matrix completion for multi-label image classification","authors":"Ricardo S Cabral, Fernando De La Torre, João P Costeira, Alexandre Bernardino","year":"2011","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Castillo and Melin, 2005] Oscar Castillo and Patricia Melin. Hybrid intelligent systems for pattern recognition using soft computing: An evolutionary approach for neural networks and fuzzy systems. Springer, New York, 2005.","title":"Hybrid intelligent systems for pattern recognition using soft computing: An evolutionary approach for neural networks and fuzzy systems","authors":"Melin Castillo, Oscar Castillo, Patricia Melin","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Gayar et al., 2006] Neamat El Gayar, Friedhelm Schwenker, and Günther Palm. A study of the ro- bustness of knn classifiers trained using soft labels. In Proceedings of the 2nd International Conference on Artificial Neural Network in Pattern Recognition, pages 67-80, Ulm, Germany, 2006.","title":"Neamat El Gayar, Friedhelm Schwenker, and Günther Palm. A study of the robustness of knn classifiers trained using soft labels","authors":"Gayar","year":"2006","venue":"Proceedings of the 2nd International Conference on Artificial Neural Network in Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Geng and Xia, 2014] Xin Geng and Yu Xia. Head pose es- timation based on multivariate label distribution. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1837-1842, Columbus, OH, 2014.","title":"Head pose estimation based on multivariate label distribution","authors":"Xin Geng, Yu Xia","year":"2014","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Geng et al., 2014] Xin Geng, Qin Wang, and Yu Xia. Facial age estimation by adaptive label distribution learning. In Proceedings of the 22nd International Conference on Pat- tern Recognition, pages 4465-4470, Stockholm, Sweden, 2014.","title":"Facial age estimation by adaptive label distribution learning","authors":"Xin Geng, Qin Wang, Yu Xia","year":"2014","venue":"Proceedings of the 22nd International Conference on Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Geng, 2016] Xin Geng. Label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 28(7):1734-1748, 2016.","title":"Label distribution learning","authors":"Xin Geng, Geng","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gibaja and Ventura, 2015] Eva Gibaja and Sebastian Ventu- ra. A tutorial on multilabel learning. ACM Computing Surveys, 47(3):1-38, 2015.","title":"Eva Gibaja and Sebastian Ventura. A tutorial on multilabel learning","authors":"Ventura Gibaja","year":"2015","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hou et al., 2016] Peng Hou, Xin Geng, and Min-Ling Zhang. Multi-label manifold learning. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, pages 1680-1686, Phoenix, AZ, 2016.","title":"Multi-label manifold learning","authors":"Peng Hou, Xin Geng, Min-Ling Zhang","year":"2016","venue":"Proceedings of the 30th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jiang et al., 2006] Xiufeng Jiang, Zhang Yi, and Jian Cheng Lv. Fuzzy svm with a new fuzzy membership func- tion. Neural Computing & Applications, 15(3-4):268-276, 2006.","title":"Fuzzy svm with a new fuzzy membership function","authors":"Jiang","year":"2006","venue":"Neural Computing & Applications","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2015] Yu-Kun Li, Min-Ling Zhang, and Xin Geng. Leveraging implicit relative labeling-importance informa- tion for effective multi-label learning. In Proceedings of the 15th IEEE International Conference on Data Mining, pages 251-260, Atlantic City, NJ, 2015.","title":"Leveraging implicit relative labeling-importance information for effective multi-label learning","authors":"Yu-Kun Li, Min-Ling Zhang, Xin Geng","year":"2015","venue":"Proceedings of the 15th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lo et al., 2011] Hung-Yi Lo, Ju-Chiang Wang, Hsin-Min Wang, and Shou-De Lin. Cost-sensitive multi-label learn- ing for audio tag annotation and retrieval. IEEE Transac- tions on Multimedia, 13(3):518-529, 2011.","title":"Cost-sensitive multi-label learning for audio tag annotation and retrieval","authors":"Hung-Yi Lo, Ju-Chiang Wang, Hsin-Min Wang, Shou-De Lin","year":"2011","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Nocedal and Wright, 2006] Jorg Nocedal and Stephen J Wright. Numerical optimization. Springer, New York, 2006.","title":"Numerical optimization","authors":"Jorg Nocedal, Stephen J Wright","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Roweis and Saul, 2000] Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally lin- ear embedding. Science, 290(5500):2323-2326, 2000.","title":"Nonlinear dimensionality reduction by locally linear embedding","authors":"Sam T Roweis, Lawrence K Saul","year":"2000","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Rubin et al., 2012] Timothy N. Rubin, America Chamber- s, Padhraic Smyth, and Mark Steyvers. Statistical topic models for multi-label document classification. Machine Learning, 88(1-2):157-208, 2012.","title":"America Chambers, Padhraic Smyth, and Mark Steyvers. Statistical topic models for multi-label document classification","authors":"Timothy N Rubin","year":"2012","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Smola, 1999] Alex J. Smola. Learning with kernels. Ph.D. Thesis, GMD, Birlinghoven, German, 1999.","title":"Learning with kernels","authors":"Alex J Smola","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Tsoumakas and Katakis, 2006] Grigorios Tsoumakas and Ioannis Katakis. Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3):1-13, 2006.","title":"Grigorios Tsoumakas and Ioannis Katakis. Multi-label classification: An overview","authors":"Katakis Tsoumakas","year":"2006","venue":"International Journal of Data Warehousing and Mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang and Zhang, 2008] Fei Wang and Changshui Zhang. Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering, 20(1):55-67, 2008.","title":"Label propagation through linear neighborhoods","authors":"Wang, Zhang ; ] Fei Wang, Changshui Zhang","year":"2008","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wang et al., 2011] Jingdong Wang, Yinghai Zhao, Xiuqing Wu, and Xian-Sheng Hua. A transductive multi-label learning approach for video concept detection. Pattern Recognition, 44(10):2274-2286, 2011.","title":"A transductive multi-label learning approach for video concept detection","authors":"Jingdong Wang, Yinghai Zhao, Xiuqing Wu, Xian-Sheng Hua","year":"2011","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhang and Zhou, 2014] Min-Ling Zhang and Zhi-Hua Zhou. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8):1819-1837, 2014.","title":"A review on multi-label learning algorithms","authors":"Zhou ; ] Min-Ling Zhang, Zhi-Hua Zhang, Zhou","year":"2014","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zhou et al., 2015] Ying Zhou, Hui Xue, and Xin Geng. E- motion distribution recognition from facial expressions. In Proceedings of the 23rd ACM International Conference on Multimedia, pages 1247-1250, Brisbane, Australia, 2015.","title":"Emotion distribution recognition from facial expressions","authors":"Ying Zhou, Hui Xue, Xin Geng","year":"2015","venue":"Proceedings of the 23rd ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhu and Goldberg, 2009] Xiaojin Zhu and Andrew B. Goldberg. Introduction to semi-supervised learning. Syn- thesis Lectures on Artificial Intelligence and Machine Learning, 3(1):1-130, 2009.","title":"Introduction to semi-supervised learning","authors":"Xiaojin Zhu, Andrew B Goldberg","year":"2009","venue":"Synthesis Lectures on Artificial Intelligence and Machine Learning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhu et al., 2005] Xiaojin Zhu, John Lafferty, and Ronald Rosenfeld. Semi-supervised learning with graphs. Carnegie Mellon University, language technologies insti- tute, school of computer science, 2005.","title":"Semi-supervised learning with graphs","authors":"Xiaojin Zhu, John Lafferty, Ronald Rosenfeld","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}