重新审视部分多标签学习在高秩性质下的稀疏性约束
Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
对做多标签视觉识别（如多物体图像标注）的研究者，这篇论文给出了一个可复用的思路：用噪声标签矩阵的稀疏性去诱导预测标签矩阵的高秩性，从而绕开传统低秩假设与稀疏假设相互冲突的问题，可直接嵌入多标签分类或弱监督标注清洗流程。
原文依据 the sparsity constraint contributes to the high-rank property of the predicted label matrix
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论部分多标签学习（PML）将多标签学习范式扩展到每个样本都关联一个候选标签集的场景，该候选标签集同时包含真实标签和噪声标签。现有的 PML 方法通常依赖两个假设：噪声标签矩阵的稀疏性，以及真实标签矩阵的低秩性。然而，这两个假设本质上相互冲突，并且在真实场景中并不实用，因为真实标签矩阵通常是满秩或接近满秩的。为解决这些局限，我们证明稀疏性约束有助于预测标签矩阵获得高秩性质。基于此，我们提出一种新方法 Schirn，它在对噪声标签矩阵施加稀疏性约束的同时，对预测标签矩阵施加高秩性质。大量实验表明，Schirn 相比最优方法具有更优的性能，验证了其在应对真实世界 PML 挑战方面的有效性。
英文原文摘要 1013 字符
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.