Unsupervised feature selection algorithm based on L 2,p-norm feature reconstruction.

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 exhibi

SUBMITTER: Liu W 

PROVIDER: S-EPMC11875355 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature