三角距离相关性的标签分布学习

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。

Label distribution learning based on triangular distance correlation

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.