Robust Low-Rank Subspace Learning for MultiLabel Feature Selection with Global-Local Correlation Modeling

Multi-label classification faces significant challenges from high-dimensional features and complex label dependencies.Traditional feature selection methods often fail to capture these dependencies effectively or suffer from high computational costs.This paper proposes a novel Robust Low-Rank Subspace Learning (RLRSL) framework for multi-label feature selection.Our method integrates global label correlations and local feature structures within a unified objective function, utilizing Schatten-p norm for low-rank subspace learning, 𝑛𝑜 𝑚 for joint feature sparsity, and manifold regularization for local geometry preservation.We develop an efficient optimization algorithm to solve the resulting non-convex problem.Comprehensive experiments on seven benchmark datasets demonstrate that RLRSL consistently outperforms state-of-the-art methods across multiple evaluation metrics, including ranking loss, multi-label accuracy, and F1-score, using both ML-* k * NN and SVM classifiers.The results confirm the robustness, efficiency, and superior generalization capability of our proposed approach