Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label data is always confronted with the curse of high-dimensional features. Feature selection has been shown to effectively improve learning performance by selecting discriminative features. Conventional multi-label feature selection only focuses on associating input features with corresponding labels while neglecting the potential structural information, i.e., instance correlations and label correlations. To tackle this problem, we propose manifold learning with structured subspace for multi-label feature selection. Specifically, we first uncover a latent subspace for a more compact and accurate data representation, and take advantage of the subspace to explore the correlations among instances. Then, we explore label correlations in manifold learning to guarantee the global and local structural consistency of labels. Besides, $l_{2,1}$-norm is introduced into loss function and sparse regularization to facilitate feature selection process. A detail optimization algorithm is presented to solve the objective function of the proposed method. Extensive experiments on real-world data show the superiority of the proposed method under various metrics.

如今，多标签学习在实际应用中无处不在，其中多标签数据总是面临着高维特征的诅咒。特征选择已被证明可以通过选择判别特征来有效提高学习性能。传统的多标签特征选择只关注将输入特征与相应的标签相关联，而忽略了潜在的结构信息，即实例相关性和标签相关性。为了解决这个问题，我们提出了具有结构化子空间的流形学习，用于多标签特征选择。具体来说，我们首先发现一个潜在的子空间，以获得更紧凑和准确的数据表示，并利用子空间来探索实例之间的相关性。然后，我们探索流形学习中的标签相关性，以保证标签的全局和局部结构一致性。除了，${升}_{2,1}$-norm 被引入损失函数和稀疏正则化以促进特征选择过程。提出了一种详细的优化算法来求解所提出方法的目标函数。对真实世界数据的大量实验表明，该方法在各种指标下的优越性。