基于实例结构的不完备多标签学习

陈天柱; 李凤华; 郭云川; 李子孚

doi:10.11959/j.issn.1000-436x.2021186

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基于实例结构的不完备多标签学习

学术论文 | 更新时间：2024-06-05

- 基于实例结构的不完备多标签学习
- Instance structure based multi-label learning with missing labels
- 通信学报 2021年42卷第11期页码：121-132
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100049
  3. 中国电子科技集团公司信息科学研究院，北京 100086
- 作者简介：
  
  [ "陈天柱（1987− ），男，河北秦皇岛人，博士，中国电子科技集团公司工程师，主要研究方向为自然语言处理" ]
  [ "李凤华（1966− ），男，湖北浠水人，博士，中国科学院信息工程研究所研究员、博士生导师，主要研究方向为网络与系统安全、信息保护、隐私计算" ]
  [ "郭云川（1977− ），男，四川营山人，博士，中国科学院信息工程研究所正高级工程师、博士生导师，主要研究方向为访问控制、网络安全" ]
  [ "李子孚（1992− ），女，内蒙古赤峰人，博士，中国科学院信息工程研究所工程师，主要研究方向为网络与系统安全、访问控制" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB2100402);国家自然科学基金资助项目(U1836203);山东省重点研发计划基金资助项目(2019JZY020127)
- DOI：10.11959/j.issn.1000-436x.2021186
  中图分类号： TP302
- 网络首发：2021-11，
  
  纸质出版：2021-11-25
- 稿件说明：
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陈天柱, 李凤华, 郭云川, 等. 基于实例结构的不完备多标签学习[J]. 通信学报, 2021,42(11):121-132.

Tianzhu CHEN, Fenghua LI, Yunchuan GUO, et al. Instance structure based multi-label learning with missing labels[J]. Journal on Communications, 2021, 42(11): 121-132.
陈天柱, 李凤华, 郭云川, 等. 基于实例结构的不完备多标签学习[J]. 通信学报, 2021,42(11):121-132. DOI： 10.11959/j.issn.1000-436x.2021186.

Tianzhu CHEN, Fenghua LI, Yunchuan GUO, et al. Instance structure based multi-label learning with missing labels[J]. Journal on Communications, 2021, 42(11): 121-132. DOI： 10.11959/j.issn.1000-436x.2021186.

摘要

针对现有标签缺失下多标签学习方案未能有效解决标签缺失的问题，提出了基于实例结构的不完备多标签学习方案，考虑实例特征和标签结构特点，利用数据标签向量几何相似度来补全缺失标签，利用加权排序来降低正关系学为负关系所带来的模型偏差，并利用低秩结构来俘获模型低秩结构。具体地，通过确保数据预测标签几何相似度与数据标签几何相似度的一致性来俘获数据流型结构；通过度量完备标签下和不完备标签下的排序损失来区分标签与实例的相关程度。实验结果表明，所提方案优于典型的标签缺失下的多标签学习方案，甚至在一些评估标准下其精度比最好对比方案提升了10%以上。

Abstract

To address the problem that the existing methods in multi-label learning did not efficiently deal with the problems

the instance structure based multi-label learning scheme with missing labels was proposed.By considering the feature and label structure of instance

the similarity of label vectors were exploit to fill the missing labels and the weight rank loss was exploit to reduce the model bias.Meanwhile

the weight rank loss was also exploit to reduce the model bias.More specially

the manifold structure was capture by forcing the consistency of the geometry similarity of labels and one of the predicted labels.By measuring ranking loss for complete labels and incomplete labels

the relevance of label was distinguish to instance.Experiment results show that the superior performances of the proposed approach compared with the state-of-the-art methods and the accuracy is improved by more than 10% compared with the best comparison scheme under some evaluation criteria.

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references

LI Y M , XU Z L , ZHANG Z F . Learning with incomplete labels [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2018 : 3588 - 3595 .

WU B Y , LIU Z L , WANG S F , et al . Multi-label learning with missing labels [C ] // Proceedings of 2014 22nd International Conference on Pattern Recognition . Piscataway:IEEE Press , 2014 : 1964 - 1968 .

LI X , ZHAO F P , GUO Y H . Conditional restricted Boltzmann machines for multi-label learning with incomplete labels [C ] // Proceedings of the International Conference on Artificial Intelligence and Statistics . Massachusetts:MIT Press , 2015 : 635 - 643 .

WU B Y , LYU S W , GHANEM B . ML-MG:multi-label learning with missing labels using a mixed graph [C ] // Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2015 : 4157 - 4165 .

ZHANG Y , SCHNEIDER J G . Multi-label output codes using canonical correlation analysis [C ] // Proceedings of International Conference on Artificial Intelligence and Statistics . Berlin:Springer , 2011 : 873 - 882 .

YU H F , JAIN P , KAR P , et al . Large-scale multi-label learning with missing labels [C ] // Proceedings of the International Conference on Machine Learning . Saarland:DBLP , 2014 : 593 - 601 .

WESTON J , BENGIO S , USUNIER N . WSABIE:scaling up to large vocabulary image annotation [C ] // Proceedings of the International Joint Conference on Artificial Intelligence . San Francisco:Margan Kaufmann , 2011 : 1 - 7 .

GONG Y , JIA Y , LEUNG T , et al . Deep convolutional ranking for multi-label image annotation [J ] . arXiv Preprint,arXiv:1312.4894 , 2013 .

ZHU Y , KWOK J T , ZHOU Z H . Multi-label learning with global and local label correlation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2018 , 30 ( 6 ): 1081 - 1094 .

HUANG J , QIN F , ZHENG X , et al . Improving multi-label classification with missing labels by learning label-specific features [J ] . Information Sciences , 2019 , 492 : 124 - 146 .

TAI F , LIN H T . Multi-label classification with principle label space transformation [J ] . Neural Computation , 2012 , 24 ( 9 ): 2508 - 2542 .

ZHANG Y , SCHNEIDER J G . Maximum margin output coding [C ] // Proceedings of the International Conference on Machine Learning . New York:ACM Press , 2012 : 1 - 8 .

JIN R , WANG S J , ZHOU Z H . Learning a distance metric from multi-instance multi-label data [C ] // Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2009 : 896 - 902 .

JING L P , YANG L , YU J , et al . Semi-supervised low-rank mapping learning for multi-label classification [C ] // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2015 : 1483 - 1491 .

LI Y C , YANG Y L . Label embedding for multi-label classification via dependence maximization [J ] . Neural Processing Letters , 2020 , 52 ( 2 ): 1651 - 1674 .

GUPTA V , WADBUDE R , NATARAJAN N , et al . Distributional semantics meets multi-label learning [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2019 , 33 : 3747 - 3754 .

LIU W W , SHEN X B . Sparse extreme multi-label learning with oracle property [C ] // Proceedings of the International Conference on Machine Learning . New York:ACM Press , 2019 : 4032 - 4041 .

USUNIER N , BUFFONI D , GALLINARI P . Ranking with ordered weighted pairwise classification [C ] // Proceedings of the 26th Annual International Conference on Machine Learning . New York:ACM Press , 2009 : 1057 - 1064 .

DURAND T , THOME N , CORD M . MANTRA:minimum maximum latent structural SVM for image classification and ranking [C ] // Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2015 : 2713 - 2721 .

RECHT B , FAZEL M , PARRILO P A . Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization [J ] . SIAM Review , 2010 , 52 ( 3 ): 471 - 501 .

BENGIO Y , GOODFELLOW I , COURVILLE A . Deep learning [M ] . The MIT Press , 2016 .

BELKIN M , NIYOGI P , SINDHWANI V . Manifold regularization:a geometric framework for learning from labeled and unlabeled examples [J ] . Journal of Machine Learning Research , 2006 , 7 ( 1 ): 2399 - 2434 .

WANG J , SHI D M , CHENG D S , et al . LRSR:low-rank-sparse representation for subspace clustering [J ] . Neurocomputing , 2016 , 214 : 1026 - 1037 .

CAI J F , CANDÈS E J , SHEN Z W . A singular value thresholding algorithm for matrix completion [J ] . SIAM Journal on Optimization , 2010 , 20 ( 4 ): 1956 - 1982 .

FAN R E , CHANG K W , HSIE C J , et al . LIBLINEAR:a library for large linear classification [J ] . Journal of Machine Learning Research , 2008 , 9 : 1871 - 1874 .

ZHANG M L , ZHOU Z H . A review on multi-label learning algorithms [J ] . IEEE Transactions on Knowledge and Data Engineering , 2014 , 26 ( 8 ): 1819 - 1837 .

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