arXiv：cs.LG· Jingjing Liu, Pei Tai, Xianchao Xiu, Wanquan Liu·· 4 小时前
MDUFS-Net：基于流形深度展开 PCA 的无监督特征选择网络
Learning to Select Features with Manifold Deep Unfolding PCA
AI 导读
研究人员提出流形深度展开特征选择网络 MDUFS-Net，将 ℓ2,0 范数引入经典 PCA 框架，并把基于 ADMM 的迭代算法展开为端到端可训练网络以实现参数自动学习。该研究还引入流形优化策略提升数值鲁棒性，并扩展出采用 ℓ0 范数的增强版 MDUFS-Net+。实验表明两者在无监督特征选择任务上优于现有方法，聚类表现同样出色，代码已开源。
正文
Abstract:Unsupervised feature selection (UFS) is of great significance in image processing and pattern recognition. Nevertheless, existing model-based methods often rely on manually tuned hyperparameters, while deep learning-based methods generally require massive training data. To address these issues, this paper proposes an efficient manifold deep unfolding feature selection network (MDUFS-Net). Technically, we first incorporate the $\ell_{2,0}$-norm into the classic principal component analysis (PCA) framework, then unfold the alternating direction method of multipliers (ADMM)-based iterative algorithm into an end-to-end trainable network, thereby enabling automatic parameter learning. Moreover, a manifold optimization strategy is developed to improve the numerical robustness. On this basis, we further extend MDUFS-Net into an enhanced variant called MDUFS-Net+ by introducing the $\ell_{0}$-norm for more discriminative feature representations. Extensive experiments validate that our proposed MDUFS-Net and MDUFS-Net+ outperform state-of-the-art methods in the UFS task, with promising performance in clustering. Our code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19436 [cs.CV] |
| (or arXiv:2610.19436v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19436 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xianchao Xiu [view email]
[v1]
Wed, 14 Oct 2026 02:28:39 UTC (1,444 KB)
来源：arXiv：cs.LG · arxiv.org