JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (7): 69-83.doi: 10.6040/j.issn.1671-9352.7.2024.452

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Multi-label feature selection with label manifold and dynamic graph constraints

WU Xiaojun1, CHEN Yidan2, HAO Yaojun1, SONG Changwei3, HE Deqing4   

  1. 1. Xinzhou Normal University, Xinzhou 034000, Shanxi, China;
    2. Henan Open University, Zhengzhou 450046, Henan, China;
    3. College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, Henan, China;
    4. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
  • Published:2025-07-01

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Abstract: Multi-label feature selection algorithms with label manifolds and dynamic graph constraints are proposed by integrating adaptive dynamic graph technique and label manifold into an improved linear mapping learning framework. In this algorithm, an improved matrix decomposition technique based on feature self-representation improves the linear mapping model and decouples the correlation between features and labels as well as between different labels. An adaptive dynamic graph technique with Laplace rank constraints is designed to learn a high-quality feature similarity graph. A label manifold based on label relevance is constructed to fully incorporate label information into the training of the algorithm. Numerous experimental results verify that the adaptive dynamic graph technique can effectively improve the quality of the graph matrix and the effectiveness of the proposed algorithm in addressing the multi-label feature selection problem.

Key words: multi-label learning, feature selection, manifold learning, adaptive learning, dynamic graph learning

CLC Number: 

  • TP181
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