Label Distribution Learning via Sample Sparse Representation

doi:10.7652/xjtuxb202011017

您当前的位置：

首页 >

文章列表页 >

Label Distribution Learning via Sample Sparse Representation

更新时间：2025-07-09

- Label Distribution Learning via Sample Sparse Representation
- Vol. 54, Issue 11, Pages: 139-148(2020)
- 作者机构：
  
  西安交通大学软件学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202011017
  CLC： TP181
- Online First：10 November 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Label Distribution Learning via Sample Sparse Representation[J]. 2020, 54(11): 139-148.
DOI：

Label Distribution Learning via Sample Sparse Representation[J]. 2020, 54(11): 139-148. DOI： 10.7652/xjtuxb202011017.

摘要

针对传统标记分布学习算法借助标记的全局相关性信息

忽略仅存于部分样本范围内标记局部相关性的问题

提出了一种基于样本稀疏表达的标记分布学习算法。借助样本点的自表达性质

建立稀疏表达优化模型

挖掘样本局部相关性信息。通过设计的标记分布目标函数约束

将得到的稀疏系数引入标记空间中

并将其作为隐含的标记空间局部相关性预测值

帮助标记分布模型的训练。使用交替方向乘子法求解样本稀疏系数

使用有限内存拟牛顿法求解标记分布目标函数

通过最大熵模型生成实例的标记分布预测值。在11个真实数据集上进行实验

并与7个现有标记分布学习算法进行对比。结果表明:所提算法在不同评价指标下的55次对比实验中取得了1.52的平均排名; 面部表情数据集SBU-3DFE上

以相对熵衡量的表情判别准确度较标记分布学习问题转换算法PT-SVM、适应性算法AA-kNN及专用算法LDLLC的分别提高了3.10%、2.53%、2.48%; 与传统标记分布学习算法相比

所提算法能够有效挖掘并利用标记局部相关性

具有良好的标记分布预测精度

且在不同类型的真实数据集上均能表现稳定。

Abstract

The traditional label distribution learning algorithms utilize the label correlation in a global way but ignore the local correlation. An algorithm called label distribution learning via sample sparse representation(LDL-SR)is proposed. Particularly

to capture the sample local correlation

LDL-SR uses the self-expressiveness property of samples to establish a sparse representation optimization model. Then

by the well-designed objective function

the obtained sparse coefficient is introduced into the label space to predict the correlation of the labels to help the training of the label distribution model. The sparse representation optimization model can be solved with alternating direction multiplier method(ADMM)

and limited memory quasi-Newton method(L-BFGS)is chosen to optimize the target function of the label distribution model. Finally

LDL-SR calculates the predicted label distribution via the maximum entropy model. Compared with 7 existing label distribution learning algorithms

the experimental results on 11 real-world datasets show that this algorithm achieves an average ranking of 1.52 in 55 comparative experiments under different evaluation measures. Compared with the PT-SVM

AA-kNN

and the LDLLC algorithms on facial expression dataset SBU-3DFE

the accuracy of expression discrimination is enhanced by 3.10%

2.53% and 2.48% respectively. It is found that LDL-SR can achieve good prediction accuracy and stable performance on different real-world datasets.

关键词

Keywords

references

READ J, PFAHRINGER B, HOLMES G. Multi-label classification using ensembles of pruned sets [C]∥Proceedings of the 2008 8th IEEE International Conference on Data Mining. Piscataway, NJ, USA: IEEE, 2008: 995-1000.

GENG X, SMITH-MILES K, ZHOU Z H. Facial age estimation by learning from label distributions [C]∥Proceedings of the 24th AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2010: 451-456.

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.

GENG Xin. Label distribution learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1734-1748.

WANG J, GENG X. Theoretical analysis of label distribution learning [C]∥Peoceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2019: 5256-5263.

WANG Jing, GENG Xin. Classification with label distribution learning [C]∥Proceedings of the 28th International Joint Conference on Artificial Intelligence. California, USA: International Joint Conferences on Artificial Intelligence Organization, 2019: 3712-3718.

XU Suping, SHANG Lin, SHEN Furao. Latent semantics encoding for label distribution learning [C]∥Proceedings of the 28th International Joint Conference on Artificial Intelligence. California, USA: International Joint Conferences on Artificial Intelligence Organization, 2019: 3982-3988.

REN Tingting, JIA Xiuyi, LI Weiwei, et al. Label distribution learning with label-specific features [C]∥Proceedings of the 28th International Joint Conference on Artificial Intelligence. California, USA: International Joint Conferences on Artificial Intelligence Organization, 2019: 3318-3324.

ZHOU Ying, XUE Hui, GENG Xin. Emotion distribution recognition from facial expressions [C]∥Proceedings of the 23rd ACM International Conference on Multimedia. New York, USA: ACM, 2015: 1247-1250.

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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016: 638-647.

JIA X, LI W, LIU J, et al. Label distribution learning by exploiting label correlations [C]∥Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2018: 3310-3317.

赵权, 耿新. 标记分布学习中目标函数的选择 [J]. 计算机科学与探索, 2017, 11(5): 708-719.

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.

GAO Binbin, ZHOU Hongyu, WU Jianxin, et al. Age estimation using expectation of label distribution learning [C]∥Proceedings of the 27th International Joint Conference on Artificial Intelligence. California, USA: International Joint Conferences on Artificial Intelligence Organization, 2018: 712-718.

GAO Binbin, XING Chao, XIE Chenwei, et al. Deep label distribution learning with label ambiguity [J]. IEEE Transactions on Image Processing, 2017, 26(6): 2825-2838.

ZHANG H Y, ZHANG Y, GENG X, Practical age estimation using deep label distribution learning [J/OL]. Frontiers of Computer Science. [2020-04-10]. https: ∥doi.org/10.1007/s11704-020-8272-4.

ZHANG Zhaoxiang, WANG Mo, GENG Xin. Crowd counting in public video surveillance by label distribution learning [J]. Neurocomputing, 2015, 166: 151-163.

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.

GENG Xin. Label distribution learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1734-1748.

BERGER A L, DELLA PIETRA V J, DELLA PIETRA S A. A maximum entropy approach to natural language processing [J]. Computational Linguistics, 1996, 22(1): 39-71.

YANG Xu, GAO Binbin, XING Chao, et al. Deep label distribution learning for apparent age estimation [C]∥Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop(ICCVW). Piscataway, NJ, USA: IEEE, 2015: 344-350.

PENG Z, ZHOU Z. Label distribution learning by optimal transport [C]∥Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2018: 4506-4513.

GENG Xin, WANG Qin, XIA Yu. Facial age estimation by adaptive label distribution learning [C]∥Proceedings of the 2014 22nd International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2014: 4465-4470.

CHA S 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.

ELHAMIFAR E, VIDAL R. Sparse subspace clustering: algorithm, theory, and applications [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2765-2781.

BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers [J]. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122.

NOCEDAL J, WRIGHT S J. Numerical optimization [M]. Cham, Germany: Springer, 1999: 176-184.

REN Tingting, JIA Xiuyi, LI Weiwei, et al. Label distribution learning with label correlations via low-rank approximation [C]∥Proceedings of the 28th International Joint Conference on Artificial Intelligence. California, USA: International Joint Conferences on Artificial Intelligence Organization, 2019: 3325-3331.

程士卿,郝问裕,李晨,等.低秩张量分解的多视角谱聚类算法.2020,54(3):119-125.[doi:10.7652/xjtuxb202003015]

吴静然,丁恩杰,崔冉,等.采用多尺度注意力机制的旋转机械故障诊断方法.2020,54(2):51-58.[doi:10.7652/xjtuxb 202002007]

张志强,孙若斌,徐冠基,等.采用非相关字典学习的滚动轴承故障诊断方法.2019,53(6):29-34.[doi:10.7652/xjtuxb 201906005]

余星达,陈文杰,王鼎,等.非接触式身份识别的深度学习算法.2019,53(4):122-127.[doi:10.7652/xjtuxb201904018]

傅翠娇,钱德沛,栾钟治.采用机器学习算法的软件能耗感知模型及其应用.2018,52(12):70-76.[doi:10.7652/xjtuxb 201812011]

张西宁,雷威,杨雨薇,等.采用自适应基因粒子群算法优化隐马尔科夫模型的方法及应用.2018,52(8):1-8.[doi:10.7652/xjtuxb201808001]

颛孙少帅,杨俊安,刘辉,等.采用双层强化学习的干扰决策算法.2018,52(2):63-69.[doi:10.7652/xjtuxb201802010]

贾晓琳,樊帅帅,罗雪,等.应用非线性加权的集成学习软件缺陷序列预测算法.2017,51(7):156-161.[doi:10.7652/xjtuxb201707022]

王万召,王杰.采用限定记忆极限学习机的过热汽温逆建模研究.2014,48(2):32-37.[doi:10.7652/xjtuxb201402006]

冯斌,梅雪松,杨军,等.数控机床摩擦误差自适应补偿方法研究.2013,47(11):65-69.[doi:10.7652/xjtuxb201311012]

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on the Thermodynamic Properties in the Anomalous Region of Supercritical Water

Numerical Simulation of Heat-Moisture Barrier Performance of a Horizontal Air Curtain in Cold Storage

Sensitivity Analysis of Foil Geometrical Parameters for the Third-Generation CO2 Gas Foil Bearings Based on Design of Experiments Method

Effects of Steam Addition on the Swirl Combustion and Emission Characteristics of Cracked Ammonia

Effects of Expansion Angle Crater Film Hole on the Film Cooling Characteristics of a Double-Wall Structure

Related Author

No data

Related Institution

No data

⁰