Unsupervised feature selection plays a dominant role in the process of high-dimensional and unlabeled data. Conventional spectral-based unsupervised feature selection methods always learn the subspace based on the predefined graph which constructed by the original features. Therefore, if the data is corrupted by the noise or redundancy existing in the high-dimensional, then the graph will be incorrect and further degrade the performance of downstream tasks. In this paper, we propose a new unsupervised feature selection method, in which the graph is self-adjusting by the original graph and learned subspace, so as to be the optimal one. Besides, the uncorrelated constraint is added to enhance the discriminability of the model. To optimize the model, we propose an alternative iterative algorithm and provide strict convergence proof. Extensive experiments are conducted to evaluate the performance of our method in comparison with other SOTA methods. The proposed adaptive graph learning strategy can learn a high-quality graph with the information of data structure more accurate. Besides, the uncorrelated constraint extremely ensures the discriminability of selected features.

无监督特征选择在高维和无标签数据处理中起着主导作用。常规的基于频谱的无监督特征选择方法总是基于由原始特征构造的预定义图来学习子空间。因此，如果数据因高维中存在的噪声或冗余而损坏，则该图形将是错误的，并进一步降低下游任务的性能。在本文中，我们提出了一种新的无监督特征选择方法，该方法通过原始图和学习子空间对图进行自动调整，从而使其成为最优图。此外，添加了不相关的约束以增强模型的可分辨性。为了优化模型，我们提出了一种替代的迭代算法，并提供了严格的收敛性证明。与其他SOTA方法相比，进行了广泛的实验以评估我们方法的性能。提出的自适应图学习策略可以通过数据结构信息更准确地学习高质量图。此外，不相关的约束条件极大地确保了所选特征的可分辨性。