统一重建视角，融合样本不确定性提升多视图多标签特征选择效果
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
- 从全局重构视角统一处理视图间一致性与互补性
- 通过样本置信度与视图关系构建图结构实现精准映射
- 适合处理带不确定性的真实多标签场景，提升模型可信度
近年来，多视图多标签学习（MVML）因其贴近真实场景而受到关注。然而，如何选取有效特征以兼顾性能与效率仍是关键挑战。现有方法常分别处理视图间一致性和互补性，易因分割不清引入噪声。本文提出一种基于全局重构视角的统一模型。同时，传统特征选择忽略样本不确定性，而该问题在真实场景中普遍存在。为此，我们在重构过程中引入样本不确定性感知，增强结果可信度。全局视角通过样本间图结构、样本置信度及视图关系共同构建，重建视图与标签矩阵间建立精确映射。实验表明，该方法在多个多视图数据集上表现优异。
原文摘要 · Abstract (English)
In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets.
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