通过全局锚点共识在噪声监督下进行鲁棒多视图分类

中文摘要 AI 辅助

近年来，多视图学习因整合异构视图的互补信息而备受关注。多数现有多视图分类方法依赖准确标注来保证性能。但由于标注不完善，噪声标签在实际中普遍存在，现有方法基于此类噪声监督训练的模型所导出的细化信号会逐渐失去可靠性。为解决此问题，我们提出一种用于多视图分类的基于全局锚点的标签审核方法（GALA）以抵抗噪声标签的负面影响。具体而言，我们为每个视图中的每个类别构建一个全局锚点，它聚合了整个类别的样本，从而提供一个对个体预测不敏感的稳定参考。然后，每个视图衡量一个实例相对于最近竞争锚点与其观察标签的锚点的接近程度，并且每个视图的评估与分类器置信度融合为一个跨视图审核分数。基于审核分数，可疑样本被赋予小权重，并且仅当基于锚点的候选与分类器预测一致时，自适应校正策略才重写标签。最后，校正后的标签反过来细化锚点并监督抗噪声表示学习。在六个数据集上的大量实验表明，GALA优于八种先进方法，尤其是在高噪声率下。

英文摘要

In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most existing multi-view classification methods rely on accurate annotations to guarantee performance. However, noisy labels are ubiquitous in practice due to imperfect annotation, and the refinement signals that existing methods derive from models trained on such noisy supervision can gradually lose their reliability. To deal with this problem, we propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification to resist the negative impact of noisy labels. Specifically, we construct a global anchor for each class in every view, which aggregates the samples of the whole class and thus offers a stable reference insensitive to individual predictions. Then, each view measures how close an instance is to the anchor of its observed label relative to the nearest competing anchor, and the per-view evaluations are fused with the classifier confidence into a cross-view audit score. Based on the audit scores, suspicious samples are assigned small weights, and an adaptive correction strategy rewrites a label only when the anchor-based candidate agrees with the classifier prediction. Finally, the corrected labels in turn refine the anchors and supervise noise-robust representation learning. Extensive experiments on six datasets demonstrate that GALA outperforms eight state-of-the-art methods, especially under high noise rates.

发表机构

• College of Science, Beijing Forestry University（北京林业大学理学院）
• School of Software, North University of China（中北大学软件学院）

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