[1]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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Label distribution learning based on triangular distance correlation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 449-458 Column: 学术论文—机器感知与模式识别 Public date: 2021-05-05
Title:
Label distribution learning based on triangular distance correlation
Author(s):
HUANG Yuting;  XU Yuanyuan;  ZHANG Hengru;  MIN Fan
College of Computer Science, Southwest Petroleum University, Chengdu 610500, China
Keywords:
label distribution learning; label correlation; triangular distance; distance mapping matrix; multi-label learning; maximum entropy model; Kullback-Leibler divergence; L-BFGS method
CLC:
TP391
DOI:
10.11992/tis.202001027
Abstract:
Aiming at the representation problem of label correlation, a label distribution learning algorithm based on triangular distance correlation is proposed in this paper. First, a distance-mapping matrix is constructed to describe the mapping relationship between the label distribution and the feature matrix. Then a new triangle distance is designed to characterize the correlation between the labels. Finally, based on the label correlation, the Kullback-Leibler divergence-based objective function is designed. Results on eight datasets show that the proposed algorithm is superior in six evaluation measures in terms of accuracy compared with eight mainstream algorithms.
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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