Multi-label classification has aroused extensive attention in various fields. With the emergence of high-dimensional label space, academia has devoted to performing label embedding in recent years. Whereas current embedding approaches do not take feature space correlation sufficiently into consideration or require an encoding function while learning embedded space. Besides, few of them can be spread to track the missing labels. In this paper, we propose a Label Embedding method via Dependence Maximization (LEDM), which obtains the latent space on which the label and feature information can be embedded simultaneously. To end this, the low-rank factorization model on the label matrix is applied to exploit label correlations instead of the encoding process. The dependence between feature space and label space is increased by the Hilbert–Schmidt independence criterion to facilitate the predictability. The proposed LEDM can be easily extended the missing labels in learning embedded space at the same time. Comprehensive experimental results on data sets validate the effectiveness of our approach over the state-of-art methods on both complete-label and missing-label cases.

多标签分类在各个领域引起了广泛的关注。随着高维标签空间的出现，近年来学术界致力于进行标签嵌入。然而，当前的嵌入方法在学习嵌入空间时并未充分考虑特征空间相关性或需要编码功能。此外，它们很少可以传播来跟踪丢失的标签。在本文中，我们提出了一种通过依赖最大化（LEDM）的标签嵌入方法，该方法获得了可以同时嵌入标签和特征信息的潜在空间。为此，可以使用标签矩阵上的低秩分解模型来利用标签相关性而不是编码过程。希尔伯特-施密特独立性标准增加了特征空间和标签空间之间的依赖性，以促进可预测性。所提出的LEDM可以在学习嵌入式空间的同时轻松扩展缺少的标签。数据集上的综合实验结果验证了我们的方法在完整标签和缺失标签情况下优于最新方法的有效性。