Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labeled instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodates to more general conditions. As most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate sample correlations in both the feature space and label space. Benefit from the sample correlations, the proposed method can boost the performance of LE. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness of our methods.

中文翻译：

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具有样本相关性的广义标签增强

最近，标签分布学习（LDL）在机器学习中引起了很多关注，其中 LDL 模型是从标记实例中学习的。与单标签和多标签注释不同，标签分布通过具有不同强度的多个标签来描述实例，并适应更一般的条件。由于大多数现有的机器学习数据集仅提供逻辑标签，因此在许多实际应用中无法使用标签分布。为了解决这个问题，我们提出了两种新的标签增强方法，即具有样本相关性的标签增强（LESC）和具有样本相关性的广义标签增强（gLESC）。更具体地说，LESC 在特征空间中使用样本的低秩表示，gLESC 利用张量多秩最小化来进一步研究特征空间和标签空间中的样本相关性。受益于样本相关性，所提出的方法可以提高 LE 的性能。对 14 个基准数据集的大量实验证明了我们方法的有效性。