Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.

像单标签学习一样，多标签学习也遭受维度诅咒的困扰。由于存在高维数据，因此将特征选择作为预处理工具始终在多标签学习中发挥关键作用。尽管已经开发了多种多标签特征选择方法，但是它们忽略了考虑特征的冗余性，从而降低了学习性能。为了解决这个问题，我们提出了一种具有不相关回归和自适应光谱图的新型多标签特征选择方法。具体来说，我们首先构建具有不相关回归模型的流形框架，以寻找不相关但又具有区分性的特征，该框架还利用基于特征空间的低维表示来适应标签分布。然后，将基于信息熵的频谱图项合并到流形框架中，以确保后续学习过程中数据的局部几何结构。根据上述原理，我们设计了一个目标函数来实现多标签特征选择，并提出了一种详细的优化方法。对多个公共多标签数据集进行了综合实验，结果表明我们的方法优于其他比较方法。