Emmanuel Ntaye; Xiang-Jun Shen; Andrew Azaabanye Bayor; Fadilul-lah Yassaanah Issahaku

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

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Author(s)

Emmanuel Ntaye ^1,* Xiang-Jun Shen ¹ Andrew Azaabanye Bayor ² Fadilul-lah Yassaanah Issahaku ³

1. School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China

2. Department of Computer Science, S.D Dombo University of Business and Integrated Development Studies, Wa, 00233, Upper West, Ghana

3. School of Mathematics and Big Data, Anhui University of Science and Technology, Anhui, 232001, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.01.01

Received: 2 Jun. 2025 / Revised: 14 Oct. 2025 / Accepted: 21 Nov. 2025 / Published: 8 Feb. 2026

Index Terms

Multi-Label Classification, Low-Rank Subspace Learning, Feature Selection, Schatten-P Norm, Manifold Regularization, Global-Local Correlation

Abstract

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, l_(2,1),-norm 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

Cite This Paper

Emmanuel Ntaye, Xiang-Jun Shen, Andrew Azaabanye Bayor, Fadilul-lah Yassaanah Issahaku, "Robust Low-Rank Subspace Learning for Multi-Label Feature Selection with Global-Local Correlation Modeling", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.1, pp. 1-18, 2026. DOI:10.5815/ijem.2026.01.01

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