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<p>Partial Multi-Label Learning (PML) is a challenging weakly supervised mechanism where each instance is associated with a candidate label set that contains both ground-truth and noisy labels. PML data are typically represented in a high-dimensional feature space and suffer from noisy labels, making PML a great challenge. Meanwhile, the differences among the inherent characteristics of different labels are often overlooked. To address the above issues, we propose a two-stage Partial Multi-label Feature Selection method based on Label Disambiguation and double-regularized Sparse Regression (PMFS-LDSR). In the first stage, we operate instance-level label disambiguation via label propagation by making full use of negative labels as well as near and far neighbors. In the second stage, the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(l_{2,1}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>l</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </math></EquationSource> </InlineEquation>-norm regularization and inner product based regularization are imposed on the feature weight matrix to reduce irrelevant and redundant features, and then the discriminative label-specific and label-group-specific features are leveraged to further refine the feature weight matrix. Comprehensive experiments on both synthetic and real-world PML datasets demonstrate the superiority of PMFS-LDSR via multiple evaluations.</p>

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Partial multi-label feature selection based on label disambiguation and double-regularized sparse regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月11日

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  • Jing Chai,
  • Yemin Han,
  • Khursheed Aurangzeb,
  • Fa Zhu,
  • Wadood Abdul

摘要

Partial Multi-Label Learning (PML) is a challenging weakly supervised mechanism where each instance is associated with a candidate label set that contains both ground-truth and noisy labels. PML data are typically represented in a high-dimensional feature space and suffer from noisy labels, making PML a great challenge. Meanwhile, the differences among the inherent characteristics of different labels are often overlooked. To address the above issues, we propose a two-stage Partial Multi-label Feature Selection method based on Label Disambiguation and double-regularized Sparse Regression (PMFS-LDSR). In the first stage, we operate instance-level label disambiguation via label propagation by making full use of negative labels as well as near and far neighbors. In the second stage, the \(l_{2,1}\) l 2 , 1 -norm regularization and inner product based regularization are imposed on the feature weight matrix to reduce irrelevant and redundant features, and then the discriminative label-specific and label-group-specific features are leveraged to further refine the feature weight matrix. Comprehensive experiments on both synthetic and real-world PML datasets demonstrate the superiority of PMFS-LDSR via multiple evaluations.