Abstract
Traditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces. However, this approach struggles to adapt to diverse datasets and often fails to handle noise and outliers effectively. In this paper, we propose an unsupervised feature selection method named unsupervised feature selection algorithm based on [Formula: see text]-norm feature reconstruction (NFRFS). Employing a flexible norm to represent both the original space and the spatial distance of feature reconstruction, enhances adaptability and broadens its applicability by adjusting p. Additionally, adaptive graph learning is integrated into the feature selection process to preserve the local geometric structure of the data. Features exhibiting sparsity and low redundancy are selected through the regularization constraint of the inner product in the feature selection matrix. To demonstrate the effectiveness of the method, numerical studies were conducted on 14 benchmark datasets. Our results indicate that the method outperforms 10 unsupervised feature selection algorithms in terms of clustering performance.