SMALE实验室论文成果：多标签学习CSDN源码

摘要: 本贴列出实验室在多标签学习方向的一些工作. 主要目的是保存 bib 方便以后查阅与引用.

1. SCI 期刊论文

1.1 多标签主动学习算法 MASP

• 串并行神经网络: 串行部分进行特征提取, 并行部分进行每个标签的预测. 发表之后才发现, 它与多任务学习的基本方案一致.
• 该网络具有处理缺失标签的能力.
• 主动学习的时候根据密度和独立性选择代表样本.
• 一篇非常八股的论文, 从头到尾都是 1, 2, 3 这样的列举.

@article{Min2022Multi,
 author  = {Xue-Yang Min and Kun Qian and Ben-Wen Zhang and Guo-Jie Song and Fan Min},
 title   = {Multi-label active learning through serial--parallel neural networks},
 journal = {Knowledge-Based Systems},
 year = {2022},
 volume  = {251},
 pages   = {109226},
 doi  = {10.1016/j.knosys.2022.109226}
}
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1.2 具有双k标记恢复算法的自相关多标记学习算法

@article{Qian2022Self,
 author  = {Kun Qian and Xue-Yang Min and Yu-Sheng Cheng and Guo-Jie Song and Fan Min},
 title   = {Self-dependence multi-label learning with double k for missing labels},
 journal = {Artificial Intelligence Review},
 year = {2022},
 pages   = {1--38},
 doi  = {10.1007/s10462-022-10279-1}
}
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1.3 使用注意力机制来联合利用标签和实例信息

@article{Cheng2022Global,
 author  = {Yu-Sheng Cheng and Kun Qian and Fan Min},
 title   = {Global and local attention-based multi-label learning with missing labels},
 journal = {Information Sciences},
 year = {2022},
 volume  = {594},
 pages   = {20--42},
 doi  = {10.1016/j.ins.2022.02.022}
}
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1.4 基于余弦距离映射的多标签分布学习算法: COS-LDL

基于余弦距离映射来计算标签之间的相关性，并利用这种相关性来构建了一种新的标签分布学习方法.
引用次数 3.

@article{Zhang2020Cos,
 author  = {Heng-Ru Zhang and Yu-Ting Huang and Yuan-Yuan Xu and Fan Min},
 title   = {COS-LDL: Label distribution learning by cosine-based distance-mapping correlation},
 journal = {IEEE Access},
 year = {2020},
 volume  = {8},
 pages   = {63961--63970},
 doi  = {10.1109/ACCESS.2020.2984622}
}
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2. 国际会议论文

2.1使用 GAN 进行数据扩增的标签分布学习算法

Label Distribution Learning with Data Augmentation using Generative Adversarial Networks

3. 核心期刊论文

3.1 融合标签局部相关性的标签分布学习算法

引用次数 0

@article{Rong2022Label,
 author  = {Bin-Yuan Rong and Yuan-Yuan Xu and Ya-Lan Lv and Heng-Ru Zhang},
 title   = {Label distribution learning by fusion of local correlation of labels},
 journal = {Journal of Shandong University (Natural Science)},
 year = {2022},
 volume  = {57},
 number  = {7},
 pages   = {53--64}
 doi  = {10.6040/j.issn.1671-9352.4.2021.196}
}
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3.2 基于三角距离相关性的标签分布学习算法

引用次数 0

@article{Huang2021Label,
 author  = {Yu-Ting Huang and Yuan-Yuan Xu and  Heng-Ru Zhang and Fan Min and others},
 title   = {Label distribution learning based on triangular distance correlation},
 journal = {CAAI Transactions on Intelligent System},
 year = {2021},
 volume  = {16},
 number  = {3},
 pages   = {449--458}
 doi  = {10.11992/tis.202001027}
}
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3.3 融合结构化标签依赖性的标签分布学习算法

引用次数 1

@article{Huang2020Label,
 author  = {Yu-Ting Huang and Yuan-Yuan Xu and  Heng-Ru Zhang and Fan Min},
 title   = {Label distribution learning by exploiting structural label dependency},
 journal = {Journal of Nanjing University Natural Science},
 year = {2020},
 volume  = {56},
 number  = {4},
 pages   = {524--532},
 doi  = {10.13232/j.cnki.jnju.2020.04.010}
}
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3.4 利用多特征混沌粒子滤波的视觉目标跟踪方法

引用次数 3

@article{Ma2020Visual,
 author  = {Yuan-Yuan Ma and Zheng-Yang Dang and Heng-Ru Zhang},
 title   = {Visual target tracking method using multi feature chaotic particle filter},
 journal = {Application Research of Computers},
 year = {2020},
 volume  = {37},
 number  = {11},
 pages   = {3500--3503+3511},
 doi  = {10.19734/j.issn.1001-3695.2019.06.0244}
}
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