[1]黄雨婷,徐媛媛,张恒汝,等.三角距离相关性的标签分布学习[J].智能系统学报,2021,16(3):449-458.[doi:10.11992/tis.202001027]
HUANG Yuting,XU Yuanyuan,ZHANG Hengru,et al.Label distribution learning based on triangular distance correlation[J].CAAI Transactions on Intelligent Systems,2021,16(3):449-458.[doi:10.11992/tis.202001027]
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第3期
页码:
449-458
栏目:
学术论文—机器感知与模式识别
出版日期:
2021-05-05
- Title:
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Label distribution learning based on triangular distance correlation
- 作者:
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黄雨婷, 徐媛媛, 张恒汝, 闵帆
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西南石油大学 计算机科学学院,四川 成都 610500
- Author(s):
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HUANG Yuting, XU Yuanyuan, ZHANG Hengru, MIN Fan
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College of Computer Science, Southwest Petroleum University, Chengdu 610500, China
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- 关键词:
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标签分布学习; 标签相关性; 三角距离; 距离映射矩阵; 多标签学习; 最大熵模型; Kullback-Leibler散度; L-BFGS方法
- Keywords:
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label distribution learning; label correlation; triangular distance; distance mapping matrix; multi-label learning; maximum entropy model; Kullback-Leibler divergence; L-BFGS method
- 分类号:
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TP391
- DOI:
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10.11992/tis.202001027
- 摘要:
-
针对标签相关性的表征问题,提出一种基于三角距离相关性的标签分布学习算法。首先,构建距离映射矩阵,描述标签分布和特征矩阵之间的映射关系。其次,设计新的三角距离,以表征标签之间的相关性。最后,结合标签相关性,设计基于Kullback-Leibler散度的目标函数。在8个数据集上的实验结果表明,与8种主流算法相比,本文提出的算法在6个准确性指标上占优势。
- Abstract:
-
Aiming at the representation problem of label correlation, a label distribution learning algorithm based on triangular distance correlation is proposed in this paper. First, a distance-mapping matrix is constructed to describe the mapping relationship between the label distribution and the feature matrix. Then a new triangle distance is designed to characterize the correlation between the labels. Finally, based on the label correlation, the Kullback-Leibler divergence-based objective function is designed. Results on eight datasets show that the proposed algorithm is superior in six evaluation measures in terms of accuracy compared with eight mainstream algorithms.
更新日期/Last Update:
2021-06-25