In multi-label learning, the key problem is to capture the relationships between multiple labels, including proximities and unconformities. In this paper, we consider the relationships among multiple labels from multi-directions, including utilizing discriminative classifier, proposing a general hierarchical constraint and proximity correlation, meanwhile combining low-rank constraint, to infer a novel Multi-Directional Multi-Label learning (MDML) model. To optimize the problems involved in to the proposed models, we develop an iterative algorithms based on the alternating direction method of multipliers (ADMM) algorithm. In the simulations, the experimental results on 4 popular benchmark datasets demonstrate the superiorities of MDML model.

在多标签学习中，关键问题是捕捉多个标签之间的关系，包括接近度和不整合度。在本文中，我们考虑了来自多方向的多个标签之间的关系，包括利用判别分类器，提出一般层次约束和邻近相关，同时结合低秩约束，推断出一种新的多方向多标签学习（MDML） ） 模型。为了优化所提出模型中涉及的问题，我们开发了一种基于乘法器交替方向法（ADMM）算法的迭代算法。在模拟中，在 4 个流行的基准数据集上的实验结果证明了 MDML 模型的优越性。