Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless, LD is unavailable in many real-world applications. To obtain LD, label enhancement (LE) has emerged to recover LD from logical label. Existing LE approach have the following problems: (\textbf{i}) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (\textbf{ii}) They ignore feature redundancy and use the collected features directly. To solve (\textbf{i}), we use the topology of the feature space to generate more accurate label-confidence. To solve (\textbf{ii}), we proposed a novel supervised LE dimensionality reduction approach, which projects the original data into a lower dimensional feature space. Combining the above two, we obtain the augmented data for LE. Further, we proposed a novel nonlinear LE model based on the label-confidence and reduced features. Extensive experiments on 12 real-world datasets are conducted and the results show that our method consistently outperforms the other five comparing approaches.

翻译：标签分布（LD）通过描述度来刻画实例，从而在标签模糊情形下提供更细粒度的监督信息。然而，在许多实际应用中，LD无法直接获取。为获得LD，标签增强（LE）技术应运而生，旨在从逻辑标签中恢复LD。现有LE方法存在以下问题：（\textbf{i}）它们使用逻辑标签训练映射以获取LD，但监督信息过于松散，可能导致模型预测不准确；（\textbf{ii}）它们忽略特征冗余，直接使用采集到的原始特征。为解决（\textbf{i}），我们利用特征空间的拓扑结构生成更精确的标签置信度。为解决（\textbf{ii}），我们提出一种新型有监督LE降维方法，将原始数据投影至低维特征空间。综合上述两项技术，我们获得用于LE的增强数据。进一步，我们基于标签置信度与降维特征，提出一种新型非线性LE模型。在12个真实世界数据集上进行了大量实验，结果表明我们的方法始终优于其他五种对比方法。