A ‎robust ‎‎unsupervised ‎‎feature ‎s‎election based on ‎‎subspace ‎l‎earning and ‎‎adaptive ‎‎graph ‎‎structure

Document Type : Research Article

Authors

1 Department of Applied Mathematics‎, ‎University of Kurdistan‎, ‎Sanandaj‎, ‎Iran

2 Department of Applied Mathematics, University of Kurdistan, Sanandaj, Iran

3 School of Engineering‎, ‎RMIT University‎, ‎Melbourne‎, ‎Australia

Abstract

Feature selection is vital for improving high-dimensional data analysis by identifying a subset of representative and uncorrelated features. This paper presents an unsupervised feature selection algorithm based on subspace learning and adaptive graph structure (UFSAG). The UFSAG uses matrix factorization to preserve global data structure and incorporates local correlations into its objective function. It also integrates sample similarity graph learning to maintain data geometry. Unlike prior methods, UFSAG employs adaptive local structure learning to reduce noise and enhance feature selection. By inducing row sparsity in the feature coefficient matrix using the $\ell_{2,1}$-norm, UFSAG identifies representative features. Comparative experiments on six datasets show UFSAG's superior clustering performance over twelve state-of-the-art methods.

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Journal of Mathematical Modeling
Volume 14, Issue 1
March 2026
Pages 207-230

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