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A robust unsupervised feature selection based on subspace learning and adaptive graph structure | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 15 مهر 1404 اصل مقاله (1.12 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2025.30225.2714 | ||
| نویسندگان | ||
| Hazhir Sohrabi1؛ Shahrokh Esmaeili* 2؛ Parham Moradi3 | ||
| 1Department of Applied Mathematics, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Applied Mathematics, University of Kurdistan, Sanandaj, Iran | ||
| 3School of Engineering, RMIT University, Melbourne, Australia | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Matrix factorization؛ feature selection؛ local correlation؛ data manifold؛ clustering | ||
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آمار تعداد مشاهده مقاله: 11 تعداد دریافت فایل اصل مقاله: 6 |
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