[1]黄雨婷,徐媛媛,张恒汝,等.三角距离相关性的标签分布学习[J].智能系统学报,2021,16(3):449-458.[doi:10.11992/tis.202001027]
 HUANG Yuting,XU Yuanyuan,ZHANG Hengru,et al.Label distribution learning based on triangular distance correlation[J].CAAI Transactions on Intelligent Systems,2021,16(3):449-458.[doi:10.11992/tis.202001027]
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三角距离相关性的标签分布学习

参考文献/References:
[1] GENG Xin. Label distribution learning[J]. IEEE transactions on knowledge and data engineering, 2016, 28(7):1734-1748.
[2] JIA Xiuyi, ZHENG Xiang, LI Weiwei, et al. Facial emotion distribution learning by exploiting low-rank label correlations locally[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019:9841-9850.
[3] YANG Xu, GAO Binbin, XING Chao, et al. Deep label distribution learning for apparent age estimation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshops. Santiago, Chile, 2015:102-108.
[4] ZHANG Hengru, HUANG Yuting, XU Yuanyuan, et al. COS-LDL:label distribution learning by cosine-based distance-mapping correlation[J]. IEEE access, 2020, 8:63961-63970.
[5] 邵东恒, 杨文元, 赵红. 应用k-means算法实现标记分布学习[J]. 智能系统学报, 2017, 12(3):325-332
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
[6] 刘玉杰, 唐顺静, 高永标, 等. 基于标签分布学习的视频摘要算法[J]. 计算机辅助设计与图形学学报, 2019, 31(1):104-110
LIU Yujie, TANG Shunjing, GAO Yongbiao, et al. Label distribution learning for video summarization[J]. Journal of computer-aided design & computer graphics, 2019, 31(1):104-110
[7] 王一宾, 田文泉, 程玉胜. 基于标记分布学习的异态集成学习算法[J]. 模式识别与人工智能, 2019, 32(10):945-954
WANG Yibin, TIAN Wenquan, CHENG Yusheng. Heterogeneous ensemble learning algorithm based on label distribution learning[J]. Pattern recognition and artificial intelligence, 2019, 32(10):945-954
[8] 耿新, 徐宁. 标记分布学习与标记增强[J]. 中国科学:信息科学, 2018, 48(5):521-530
GENG Xin, XU Ning. Label distribution learning and label enhancement[J]. Scientia sinica informationis, 2018, 48(5):521-530
[9] ZHANG Mingling, ZHANG Kun. Multi-label learning by exploiting label dependency[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA, 2010:999-1007.
[10] BI Wei, KWOK J T. Multilabel classification with label correlations and missing labels[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec City, Canada, 2014:1680-1686.
[11] HUANG Shengjun, ZHOU Zhihua. Multi-label learning by exploiting label correlations locally[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence. Toronto, Canada, 2012:949-955.
[12] GENG Xin, WANG Qin, XIA Yu. Facial age estimation by adaptive label distribution learning[C]//Proceedings of the 22nd International Conference on Pattern Recognition. Stockholm, Sweden, 2014:4465-4470.
[13] ZHANG Zhaoxiang, WANG Mo, GENG Xin. Crowd counting in public video surveillance by label distribution learning[J]. Neurocomputing, 2015, 166:151-163.
[14] GENG Xin, YIN Chao, ZHOU Zhihua. Facial age estimation by learning from label distributions[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(10):2401-2412.
[15] GENG Xin, LING Miaogen. Soft video parsing by label distribution learning[C]. Proceedings of the 31th AAAI Conference on Artificial Intelligence. San Francisco, USA, 2017:1331?1337.
[16] JIA Xiuyi, LI Weiwei, LIU Junyu, et al. Label distribution learning by exploiting label correlations[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA, 2018:3310-3317.
[17] ZHENG Xiang, JIA Xiuyi, LI Weiwei. Label distribution learning by exploiting sample correlations locally[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA, 2018:4556-4563.
[18] KULLBACK S, LEIBLER R A. On information and sufficiency[J]. The annals of mathematical statistics, 1951, 22(1):79-86.
[19] DANIELSSON P E. Euclidean distance mapping[J]. Computer graphics and image processing, 1980, 14(3):227-248.
[20] S?RENSEN T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content, and its application to analyses of the vegetation on Danish commons[J]. Kongelige danske videnskabernes selskab biologiske skrifter, 1948, 5(4):1-34.
[21] GAVIN D G, OSWALD W W, WAHL E R, et al. A statistical approach to evaluating distance metrics and analog assignments for pollen records[J]. Quaternary research, 2003, 60(3):356-367.
[22] DUDA R O, HART P E, STORK D G. Pattern classification[M]. 2nd ed. New York:Wiley, 2000.
[23] DEZA E, DEZA M M. Dictionary of distances[M]. Amsterdam:Elsevier, 2006.
[24] JEGOU H, DOUZE M, SCHMID C. Hamming embedding and weak geometric consistency for large scale image search[C]//Proceedings of the 10th European Conference on Computer Vision. Marseille, France, 2008:304-317.
[25] BERGER A L, PIETRA V J D, PIETRA S A D. A maximum entropy approach to natural language processing[J]. Computational linguistics, 1996, 22(1):39-71.
[26] ZHOU Deyu, ZHANG Xuan, ZHOU Yin, et al. Emotion distribution learning from texts[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, Texas, 2016:638-647.
[27] YUAN Yaxiang. A modified BFGS algorithm for unconstrained optimization[J]. IMA journal of numerical analysis, 1991, 11(3):325-332.
[28] EISEN M B, SPELLMAN P T, BROWN P O, et al. Cluster analysis and display of genome-wide expression patterns[J]. Proceedings of the national academy of sciences of the united states of America, 1998, 95(25):14863-14868.
[29] CHA Su H. Comprehensive survey on distance/similarity measures between probability density functions[J]. International journal of mathematical models and methods in applied sciences, 2007, 1(4):300-307.
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备注/Memo

收稿日期:2020-01-20。
基金项目:国家自然科学基金项目(61902328)
作者简介:黄雨婷,硕士研究生,主要研究方向为标签分布学习和推荐系统。;徐媛媛,讲师,主要研究方向为信号处理和推荐系统。主持教育部产学合作协同育人项目2项。发表学术论文2篇;张恒汝,教授,主要研究方向为标签分布学习、粒计算、推荐系统和数据挖掘。主持四川省科技厅项目1项,参与省部级科研及教研项目3项。发表学术论文30余篇
通讯作者:张恒汝.E-mail:zhanghrswpu@163.com

更新日期/Last Update: 2021-06-25
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