Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD) for training samples is extremely costly. So recent studies often first use label enhancement (LE) to generate the estimated label distribution from the logical label and then apply external LDL algorithms on the recovered label distribution to predict the label distribution for unseen samples. But this step-wise manner overlooks the possible connections between LE and LDL. Moreover, the existing LE approaches may assign some description degrees to invalid labels. To solve the above problems, we propose a novel method to learn an LDL model directly from the logical label, which unifies LE and LDL into a joint model, and avoids the drawbacks of the previous LE methods. Extensive experiments on various datasets prove that the proposed approach can construct a reliable LDL model directly from the logical label, and produce more accurate label distribution than the state-of-the-art LE methods.

翻译：标签分布学习（LDL）是一种有效预测样本标签描述程度（即标签分布）的方法。然而，为训练样本标注标签分布（LD）的成本极高。因此，近年研究通常先使用标签增强（LE）从逻辑标签生成估计的标签分布，再应用外部LDL算法对恢复的标签分布进行预测，从而得到未见样本的标签分布。但这种分步方式忽视了LE与LDL之间可能存在的关联。此外，现有LE方法可能将部分描述程度分配给无效标签。为解决上述问题，我们提出一种直接从逻辑标签学习LDL模型的新方法，该方法将LE与LDL统一为联合模型，并避免了先前LE方法的缺陷。在多种数据集上的大量实验证明，所提方法能够直接从逻辑标签构建可靠的LDL模型，并生成比现有最优LE方法更准确的标签分布。