Label Distribution Learning Based on Hierarchical Tag Structure
Liu Kan1(),You Meilin2,Wei Lanxi1
1School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2China Mobile Communications Group Sichuan Co. Ltd., Chengdu 610041, China
[Objective] This paper focuses on the complex hierarchical relationship between tokens in label distribution learning. It enhance performance by adding the hierarchical tag structure to the label distribution learning model.[Methods] We proposed a hierarchy-based label distribution learning algorithm (H-LDL), which used conditional probability to describe the extensive and intensive tag structural relationship. We also adjusted the exact distribution of each level by the function of hierarchical weighted loss and its optimization strategy. [Results] We examined the new model on two public datasets. The Euclidean, Squared, and K-L scores decreased by 3.99%, 1.07%, and 3.10% on BU_3DFE dataset compared to the baseline model, while Intersec and Fidelity improved by 4.24% and 0.67%. On COMP dataset, the Euclidean decreased by 0.48%, but the Squared and K-L showed no significant decrease, while Intersect and Fidelity metrics increased by 0.45% and 0.02%. [Limitations] We only included two hierarchical relationships in the new model. Further research is needed for more complex hierarchical relationships. [Conclusions] A hierarchical label structure effectively improves the performance of label distribution learning.
刘勘, 游美琳, 卫兰茜. 基于层次标签结构的标记分布学习*[J]. 数据分析与知识发现, 2024, 8(2): 44-55.
Liu Kan, You Meilin, Wei Lanxi. Label Distribution Learning Based on Hierarchical Tag Structure. Data Analysis and Knowledge Discovery, 2024, 8(2): 44-55.
(Wang Yanru, Ma Huifang, Liu Haijiao, et al. A Microblog User Interest Modeling Method Based on Multi-Tag Semantic Correlation[J]. Computer Engineering & Science, 2018, 40(11): 2067-2073.)
(Geng Xin, Xu Ning, Shao Ruifeng. Label Enhancement for Label Distribution Learning[J]. Journal of Computer Research and Development, 2017, 54(6): 1171-1184.)
[5]
Ren Z C, Peetz M H, Liang S S, et al. Hierarchical Multi-Label Classification of Social Text Streams[C]// Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. 2014: 213-222.
[6]
Yan Z C, Zhang H, Piramuthu R, et al. HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. 2015: 2740-2748.
(Wang Weijun, Ning Zhiyuan, Du Yi, et al. Identifying Interdisciplinary Sci-Tech Literature Based on Multi-Label Classification[J]. Data Analysis and Knowledge Discovery, 2023, 7(1): 102-112.)
(Liu Haifeng, Liu Shousheng, Zhang Xueren, et al. A Model of Text Categorization Automatically Based on Category[J]. New Technology of Library and Information Service, 2010(4): 72-76.)
(Zhang Huaxin, Pang Jiangang. Research on Text Classification Based on SVM and KNN[J]. Journal of Modern Information, 2015, 35(5): 73-77.)
doi: 10.3969/j.issn.1008-0821.2015.05.014
(Zheng Wei, Wang Chaokun, Liu Zhang, et al. A Multi-Label Classification Algorithm Based on Random Walk Model[J]. Chinese Journal of Computers, 2010, 33(8): 1418-1426.)
doi: 10.3724/SP.J.1016.2010.01418
[11]
Wu B Y, Jia F, Liu W, et al. Multi-Label Learning with Missing Labels Using Mixed Dependency Graphs[J]. International Journal of Computer Vision, 2018, 126(8): 875-896.
doi: 10.1007/s11263-018-1085-3
[12]
Geng X, Smith-Miles K A, Zhou Z H. Facial Age Estimation by Learning from Label Distributions[C]// Proceedings of 24th AAAI Conference on Artificial Intelligence. 2010: 451-456.
[13]
Geng X, Ji R Z. Label Distribution Learning[C]// Proceedings of IEEE 13th International Conference on Data Mining. 2013. DOI:10.1109/ICDMW.2013.19.
(Shao Dongheng, Yang Wenyuan, Zhao Hong. Label Distribution Learning Based on k-means Algorithm[J]. CAAI Transactions on Intelligent Systems, 2017, 12(3): 325-332.)
Li Chan, Yang Wenyuan, Zhao Hong. Label Distribution Learning Based on Least Square Method[J]. Journal of Zhengzhou University (Natural Science Edition), 2017, 49(4): 22-27.)
[17]
Xing C, Geng X, Xue H. Logistic Boosting Regression for Label Distribution Learning[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016: 4489-4497.
[18]
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
(Wang Yibin, Li Tianli, Cheng Yusheng. Label Distribution Learning Based on Spectral Clustering[J]. CAAI Transactions on Intelligent Systems, 2019, 14(5): 966-973.)
[20]
Zhai Y S, Dai J H, Shi H. Label Distribution Learning Based on Ensemble Neural Networks[C]// Proceedings of the 25th International Conference on Neural Information Processing. 2018: 593-602.
(Zhao Quan, Geng Xin. Selection of Target Function in Label Distribution Learning[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(5): 708-719.)
doi: 10.3778/j.issn.1673-9418.1603051
[22]
Zhao W, Wang H. Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation[J]. Sensors, 2016, 16(7): 994.
doi: 10.3390/s16070994
[23]
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.
doi: 10.1109/TPAMI.2013.51
pmid: 23969385
Zeng Xueqiang, Luo Mingzhu, Chen Sufen, et al. The Facial Age Estimation Based on Adaptive Multivariate Multiple Regression[J]. Journal of Jiangxi Normal University (Natural Science Edition), 2019, 43(1): 68-75.)
(Xiang Run, Chen Sufen, Zeng Xueqiang. Facial Age Estimation Based on Multivariate Multiple Regression[J]. Journal of Shandong University (Engineering Science), 2019, 49(2): 54-60.)
[26]
Geng X, Wang Q, Xia Y. Facial Age Estimation by Adaptive Label Distribution Learning[C]// Proceedings of the 22nd International Conference on Pattern Recognition. 2014: 4465-4470.
[27]
Liu A N, Shi Y D, Jing P G, et al. Structured Low-Rank Inverse-Covariance Estimation for Visual Sentiment Distribution Prediction[J]. Signal Processing, 2018, 152: 206-216.
doi: 10.1016/j.sigpro.2018.06.001
[28]
Geng X, Hou P. Pre-release Prediction of Crowd Opinion on Movies by Label Distribution Learning[C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015: 3511-3517.
[29]
Geng X, Xia Y. Head Pose Estimation Based on Multivariate Label Distribution[C]// Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1837-1842.
[30]
Sang G L, Chen H, Huang G, et al. Unseen Head Pose Prediction Using Dense Multivariate Label Distribution[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 516-526.
[31]
Ling M G, Geng X. Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning[J]. IEEE Transactions on Image Processing, 2019, 28(11): 5691-5701.
doi: 10.1109/TIP.2019.2922818
pmid: 31226075
[32]
Zhang Z X, Wang M, Geng X. Crowd Counting in Public Video Surveillance by Label Distribution Learning[J]. Neurocomputing, 2015, 166: 151-163.
doi: 10.1016/j.neucom.2015.03.083
[33]
Liao L F, Zhang X, Zhao F Q. Multi-Branch Deformable Convolutional Neural Network with Label Distribution Learning for Fetal Brain Age Prediction[C]// Proceedings of 2020 IEEE 17th International Symposium on Biomedical Imaging. 2020: 424-427.
(Jing Yizhen, Zhao Yaoshuai, Fu Zhifeng, et al. A Prediction Algorithm of Passenger Dynamic Demand for Delayed Flights[J]. Computer Simulation, 2022, 39(6): 26-30.)
(Liu Ruixin, Liu Xinyuan, Li Chen. Label Distribution Learning Method Based on Low-Rank Representation[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(2): 146-156.)
doi: 10.16451/j.cnki.issn1003-6059.202102006
(Li Ruiyu, Zhu Jihua, Liu Xinyuan. Label Distribution Learning with Collaboration among Labels[J]. Journal of Software, 2022, 33(2): 539-554.)
[37]
Jia X Y, Li Z C, Zheng X, et al. Label Distribution Learning with Label Correlations on Local Samples[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(4): 1619-1631.
doi: 10.1109/TKDE.69
[38]
Qiu Z Y, Hu M J, Zhao H. Hierarchical Classification Based on Coarse- to Fine-Grained KNOWLEDGE Transfer[J]. International Journal of Approximate Reasoning, 2022, 149: 61-69.
doi: 10.1016/j.ijar.2022.07.002
[39]
Huang W, Chen E H, Liu Q, et al. Hierarchical Multi-Label Text Classification: An Attention-Based Recurrent Network Approach[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1051-1060.
[40]
Li Y K, Zhang M L, Geng X. Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning[C]// Proceedings of 2015 IEEE International Conference on Data Mining. 2015: 251-260.
[41]
Yin L J, Wei X Z, Sun Y, et al. A 3D Facial Expression Database for Facial Behavior Research[C]// Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. 2006: 211-216.