计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 190-197.doi: 10.11896/jsjkx.200700077
董心悦, 范瑞东, 侯臣平
DONG Xin-yue, FAN Rui-dong, HOU Chen-ping
摘要： 标记分布学习是在以标记分布标注的示例上学习的新型学习范式,近年来已成功应用于面部年龄估计、头部姿势估计和情感识别等实际场景中。在标记分布学习中,需要足够多的标记分布数据才能训练出预测性能好的模型。然而,标记分布学习有时会面临标记数据不足和注释成本太高的困境。基于边际概率分布匹配的主动标记分布学习(Active Label Distribution Learning Based on Marginal Probability Distribution Matching,ALDL-MMD)算法是针对标记分布学习注释成本过高的问题而设计的,以减少训练模型所需的标注数据量,从而降低注释成本。ALDL-MMD算法训练了一个线性回归模型,在保证其训练误差最小的同时,学习一个反映未标记数据上选点需求的稀疏向量,使选点后的训练集和未标记集的数据分布尽量相似,并对这个向量做松弛化处理,以简计算。在多个标记分布数据集上的实验结果表明,在“Canberra Metric”和“Intersection”这两个衡量标记分布的指标上,ALDL-MMD算法优于已有的主动示例选择方法,体现了其在降低注释成本方面的有效性。
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| [1] ZHANG M,ZHOU Z.A Review on Multi-Label Learning Algorithms[J].IEEE Transactions on Knowledge & Data Enginee-ring,2014,26(8):1819-1837. [2] GENG X,JI R.Label Distribution Learning[C]//IEEE International Conference on Data Mining Workshops.IEEE Computer Society,2013. [3] HE Z,LI X,ZHANG Z.Data-Dependent Label DistributionLearning for Age Estimation[J].IEEE Trans. Image Process.,2017,26(8):3846-3858. [4] KONG S,OYINI MBOUNA R.Head Pose Estima-tion from a 2-D Face Image using 3-D Face Morphing with Depth Parameters [J].IEEE Transactions on Image Processing,2015,24(6):1-1. [5] ZHANG Z L,LAI C H,LIU H,et al.Infrared Facial Expression Recognition via Gaussian-based Label Distribution Learning in the Dark Illumination Environment[J].Neurocomputing,2020,409:341-350. [6] ZHOU D Y,ZHANG X,ZHOU Y,et al.Emotion Distribution Learning from Texts[C]//Conference on Empirical Methods in Natural Language Processing.2016. [7] SEUNG H S.Query by Committee[C]//Workshop on Computational Learning Theory.ACM,1992. [8] FREUND Y,SEUNG H S,SHAMIR E,et al.Selective Sampling Using the Query by Committee Algorithm[J].Machine Learning,1997,28(2/3):133-168. [9] GU S,CAI Y,SHAN J,et al.Active Learning with Error-Correcting Output Codes[J].Neurocomputing,2019,364:182-191. [10] LINDLEY D V.On a Measure of the Information Provided by an Experiment[J].Annals of Mathematical Statistics,1956,27(4):986-1005. [11] YU K,BI J B,TRESP V,et al.Active learning via transductive experimental design[C]//International Conference on Machine Learning.2006:1081-1088. [12] PATRA S,BRUZZONE L.A cluster-assumption based batchmode active learning technique[J].Pattern Recognition Letters,2012,33(9):1042-1048. [13] BURGES C J.A Tutorial on Support Vector Machines for Pattern Recognition[J].Data Mining and Knowledge Discovery,1998,2(2):121-167. [14] MCCALLUM A,NIGAM K.A comparison of event models for naive bayes text classification[C]//National Conference on Artificial Intelligence.1998:41-48. [15] PIETRA S D,PIETRA V D,LAFFERTY J,et al.Inducing features of random fields[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(4):380-393. [16] NOCEDAL J,WRIGHT S.Numerical optimization[M].Springer Science & Business Media,2006:61-63. [17] PRINCE M.Does Active Learning Work? A Review of the Research[J].Journal of Engineering Education,2004,93(3):223-231. [18] CAI D,HE X.Manifold Adaptive Experimental Design for Text Categorization[J].IEEE Transactions on Knowledge and Data Engineering,2011,24(4):707-719. [19] SINDHWANI V,NIYOGI P,BELKIN M,et al.Beyond thepoint cloud:from transductive to semi-supervised learning[C]//International Conference on Machine Learning.2005:824-831. [20] PAN S J,TSANG I W,KWOK J T,et al.Domain Adaptation via Transfer Component Analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210. [21] GRANT M,BOYD S.CVX:Matlab software for disciplined convex programming[J].International Journal of Communications,Network and System Science,2008,1(1). [22] EISEN M B,SPELLMAN P T,BROWN P O,et al.Clusteranalysis 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. [23] CHA S H.Comprehensive Survey on Distance/ Similarity Mea-sures Between Probability Density Functions[J].International Journal of Mathematical Models and Methods in Applied Sciences,2007,1(4):300-307. |
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