CHEN C H, PATEL V M, CHELLAPPA R. Matrix Completion for Resolving Label Ambiguity[C] //2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 4110-4118.
GAO B B, XING C, XIE C W, et al. Deep Label Distribution Learning with Label Ambiguity[J]. IEEE Transactions on Image Processing, 2017, 26(6): 2825-2838. doi: 10.1109/TIP.2017.2689998
周亮, 陈辰, 李宁. 基于机器学习和经验模态分解的跨期套利研究[J]. 西南大学学报(自然科学版), 2022, 44(1): 148-159.
杭立, 车进, 宋培源, 等. 基于机器学习和图像处理技术的病虫害预测[J]. 西南大学学报(自然科学版), 2020, 42(1): 134-141.
COUR T, SAPP B, TASKAR B. Learning from Partial Labels[J]. Journal of Machine Learning Research, 2011, 12: 1501-1536.
TSOUMAKAS G, KATAKIS I. Multi-Label Classification[J]. International Journal of Data Warehousing and Mining, 2007, 3(3): 1-13. doi: 10.4018/jdwm.2007070101
ZHU Y, KWOK J T, ZHOU Z H. Multi-Label Learning with Global and Local Label Correlation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(6): 1081-1094. doi: 10.1109/TKDE.2017.2785795
ZHANG M L, WU L. Lift: Multi-Label Learning with Label-Specific Features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120. doi: 10.1109/TPAMI.2014.2339815
GENG X. Label Distribution Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1734-1748. doi: 10.1109/TKDE.2016.2545658
JIA X Y, LI W W, LIU J Y, et al. Label Distribution Learning by Exploiting Label Correlations[C] //Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans, Lousiana, USA: AAAI Press, 2018: 3310-3317.
ZHENG X, JIA X Y, LI W W. Label Distribution Learning by Exploiting Sample Correlations Locally[C] //Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, Lousiana, USA: AAAI Press, 2018: 4556-4563.
容斌元, 徐媛媛, 吕亚兰, 等. 融合标签局部相关性的标签分布学习[J]. 山东大学学报(理学版), 2022, 57(7): 53-64.
WANG J, GENG X. Label Distribution Learning by Exploiting Label Distribution Manifold[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(2): 839-852. doi: 10.1109/TNNLS.2021.3103178
KRAWCZYK B. Learning from Imbalanced Data: Open Challenges and Future Directions[J]. Progress in Artificial Intelligence, 2016, 5(4): 221-232. doi: 10.1007/s13748-016-0094-0
REN T T, JIA X Y, LI W W, et al. Label Distribution Learning with Label-Specific Features[C] //Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. California: AAAI Press, 2019: 3318-3324.
CHARTE F, RIVERA A J, DEL JESUS M J, et al. MLSMOTE: Approaching Imbalanced Multilabel Learning through Synthetic Instance Generation[J]. Knowledge-Based Systems, 2015, 89: 385-397.
TORGO L, RIBEIRO R P, PFAHRINGER B, et al. SMOTE for Regression[C] //Portuguese Conference on Artificial Intelligence. Berlin, Heidelberg: Springer, 2013: 378-389.
Branco P, Torgo L, Ribeiro R P. Smogn: A Pre-processing Approach for Imbalanced Regression[C] //Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications. Skopje, Macedonia: Microtome Publishing, 2017: 36-50.
WU G Q, TIAN Y J, LIU D L. Cost-Sensitive Multi-Label Learning with Positive and Negative Label Pairwise Correlations[J]. Neural Networks, 2018, 108: 411-423.
ZHAO X Y, AN Y X, XU N, et al. Continuous Label Distribution Learning[J]. Pattern Recognition, 2023, 133: 109056.
YANG Y Z, ZHA K W, CHEN Y C, et al. Delving into Deep Imbalanced Regression[C] //International Conference on Machine Learning. Virtual Event: Microtome Publishing, 2021: 11842-11851.
黄雨婷, 徐媛媛, 张恒汝, 等. 融合标签结构依赖性的标签分布学习[J]. 南京大学学报(自然科学), 2020, 56(4): 524-532.
NOCEDAL J, WRIGHT S J. Numerical optimization[M]. 2nd ed. New York: Springer, 2006.
R Y, G X. Sense Beauty by Label Distribution Learning[C] //Proceedings of the 32th International Joint Conference on Artificial Intelligence. Melbourne, Australia: AAAI Press, 2017: 2648-2654.
NGUYEN T V, LIU S, NI B B, et al. Sense Beauty via Face, Dressing, and/or Voice[C] //Proceedings of the 20th ACM International Conference on Multimedia. Nara, Japan: ACM, 2012: 239-248.
PENG K C, CHEN T, SADOVNIK A, et al. A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions[C] //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015: 860-868.
YANG J F, SUN M, SUN X X. Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 31(1): 224-230.
DALAL N, TRIGGS B. Histograms of Oriented Gradients for Human Detection[C] //2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005: 886-893.
STRICKER M A, ORENGO M. Similarity of Color Images[C] //Proc SPIE 2420, Storage and Retrieval for Image and Video Databases Ⅲ. San Jose, CA, United States: SPIE Press, 1995: 381-392.
LIANG L Y, LIN L J, JIN L W, et al. SCUT-FBP5500: a Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction[C] //2018 24th International Conference on Pattern Recognition (ICPR). Beijing, China: IEEE, 2018: 1598-1603.
LYONS M, AKAMATSU S, KAMACHI M, et al. Coding Facial Expressions with Gabor Wavelets[C] //Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan: IEEE, 1998: 200-205.
JIA X Y, LU Y N, ZHANG F W. Label Enhancement by Maintaining Positive and Negative Label Relation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1708-1720.
GENG X, YIN C, ZHOU Z H. Facial Age Estimation by Learning from Label Distributions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401-2412.