提出新方法缓解标签噪声与高秩矛盾，提升真实场景多标签学习效果。
Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
- 在噪声标签矩阵上加稀疏约束，同时强制预测矩阵保持高秩
- 实验证明新方法优于现有最优模型，尤其在真实数据上表现更优
- 适合处理含大量噪声标签的真实多标签学习任务
部分多标签学习（PML）将多标签学习推广到每个样本仅提供候选标签集的场景，其中包含真实标签和噪声标签。现有PML方法普遍依赖两个假设：噪声标签矩阵稀疏，真实标签矩阵低秩。然而这两个假设存在内在冲突，且在真实场景中不成立——真实标签矩阵通常为满秩或接近满秩。本文揭示了稀疏性约束会加剧预测标签矩阵的高秩特性。基于此，提出新方法Schirn，对噪声标签矩阵施加稀疏约束，同时在预测标签矩阵上强制高秩性质。大量实验表明，Schirn在性能上优于当前最优方法，有效应对真实世界中部分多标签学习的挑战。
原文摘要 · Abstract (English)
Partial Multi-Label Learning (PML) extends the multi-label learning paradigm to scenarios where each sample is associated with a candidate label set containing both ground-truth labels and noisy labels. Existing PML methods commonly rely on two assumptions: sparsity of the noise label matrix and low-rankness of the ground-truth label matrix. However, these assumptions are inherently conflicting and impractical for real-world scenarios, where the true label matrix is typically full-rank or close to full-rank. To address these limitations, we demonstrate that the sparsity constraint contributes to the high-rank property of the predicted label matrix. Based on this, we propose a novel method Schirn, which introduces a sparsity constraint on the noise label matrix while enforcing a high-rank property on the predicted label matrix. Extensive experiments demonstrate the superior performance of Schirn compared to state-of-the-art methods, validating its effectiveness in tackling real-world PML challenges.
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