Label distribution learning: A local collaborative mechanism
Label distribution learning: A local collaborative mechanism
复制标题
标签分布学习：本地协作机制
DOI：
10.1016/j.ijar.2020.02.003
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发表时间：
2020-06
影响因子：
3.9
通讯作者：
Li Chun
中科院分区：
文献类型：
--
作者：
Xu Suping;Ju Hengrong;Shang Lin;Pedrycz Witold;Yang Xibei;Li Chun
标签分布学习（LDL）是一种用于处理标签歧义的通用机器学习框架，因为它可以探索每个样本描述中不同标签的相对重要性水平。虽然已经提出了一些算法来解决LDL问题，但它们通常在一定程度上破坏了特征空间和标签空间之间的几何结构的一致性，这往往在学习任务中起着重要的作用。同时，现有的LDL算法大多只考虑预测性能，而忽略了计算代价和对噪声的鲁棒性。为了弥补上述不足，我们提出了一种新的算法，即，基于局部协作表示的标签分布学习，简称LCR-LDL。LCR-LDL将未标记样本作为其邻域构造的局部字典的协作表示，利用表示系数的判别信息重构未标记样本的标记分布。在20个真实LDL数据集上的实验结果表明，LCR-LDL算法不仅能有效地提高LDL任务的预测性能，而且具有更高的鲁棒性和更小的计算开销.这项研究提出了在LDL社区考虑计算成本和鲁棒性问题的新趋势。
Label distribution learning (LDL) is a generalized machine learning framework for dealing with label ambiguity, as it can explore the relative importance levels of different labels in the description of each sample. Although several algorithms have been proposed to solve LDL problems, they usually destroy the consistency of geometric structures between feature space and label space to a certain extent, which frequently plays a significant role in learning tasks. Meanwhile, most existing LDL algorithms only take predictive performances into consideration, while ignoring the computational cost and robustness to noises. To remedy above deficiencies, we propose a novel algorithm, i.e., Local Collaborative Representation based Label Distribution Learning, shortly LCR-LDL. In LCR-LDL, an unlabeled sample is treated as the collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample, and the discriminatory information of representation coefficients is used to reconstruct the label distribution of the unlabeled sample. Experimental results on 20 real-world LDL data sets compared with results produced by 11 state-of-the-art algorithms show that, the proposed LCR-LDL algorithm can not only effectively improve the predictive performances for LDL tasks, but also exhibit higher robustness and a lightweight computational overhead. This study suggests new trends for considering the computational cost and robustness issues in the LDL community.
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影响因子：
--
作者：
Jufeng Yang;Ming Sun;Xiaoxiao Sun
通讯作者：
Jufeng Yang;Ming Sun;Xiaoxiao Sun
DOI：
10.1007/b98874
发表时间：
2018-09
期刊：
--
影响因子：
--
作者：
J. Nocedal;Stephen J. Wright
通讯作者：
J. Nocedal;Stephen J. Wright
DOI：
10.1109/tkde.2019.2951561
发表时间：
2019-11
影响因子：
8.9
作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
通讯作者：
Min-Ling Zhang;Qian-Wen Zhang;Jun-Peng Fang;Yukun Li;Xin Geng
DOI：
10.3115/1118853.1118871
发表时间：
2002-08
期刊：
--
影响因子：
--
作者：
Robert Malouf
通讯作者：
Robert Malouf
影响因子：
11.8
作者：
Jianhua Dai;Hu Hu-Hu;Q. Hu;Wei Huang;Nenggan Zheng;Liang Liu
通讯作者：
Jianhua Dai;Hu Hu-Hu;Q. Hu;Wei Huang;Nenggan Zheng;Liang Liu