In multi-view representation learning (MVRL), the challenge of category uncertainty is significant. Existing methods excel at deriving shared representations across multiple views, but often neglect the uncertainty associated with cluster assignments from each view, thereby leading to increased ambiguity in the category determination. Additionally, methods like kernel-based or neural network-based approaches, while revealing nonlinear relationships, lack attention to category uncertainty. To address these limitations, this paper proposes a method leveraging the uncertainty of label distributions to enhance MVRL. Specifically, our approach combines uncertainty reduction based on label distribution with view representation learning to improve clustering accuracy and robustness. It initially computes the within-view representation of the sample and semantic labels. Then, we introduce a novel constraint based on either variance or information entropy to mitigate class uncertainty, thereby improving the discriminative power of the learned representations. Extensive experiments conducted on diverse multi-view datasets demonstrate that our method consistently outperforms existing approaches, producing more accurate and reliable class assignments. The experimental results highlight the effectiveness of our method in enhancing MVRL by reducing category uncertainty and improving overall classification performance. This method is not only very interpretable but also enhances the model’s ability to learn multi-view consistent information.

中文翻译：

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标签分布驱动的多视图表示学习


在多视图表示学习 （MVRL） 中，类别不确定性的挑战是巨大的。现有方法擅长在多个视图中推导出共享表示，但往往忽略了与每个视图的聚类分配相关的不确定性，从而导致类别确定的歧义增加。此外，基于内核或基于神经网络的方法等方法虽然揭示了非线性关系，但缺乏对类别不确定性的关注。为了解决这些限制，本文提出了一种利用标签分布的不确定性来增强 MVRL 的方法。具体来说，我们的方法将基于标签分布的不确定性降低与视图表示学习相结合，以提高聚类的准确性和稳健性。它最初计算样本和语义标签的视图内表示形式。然后，我们引入了一种基于方差或信息熵的新约束，以减轻类的不确定性，从而提高学习到的表示的判别能力。在各种多视图数据集上进行的广泛实验表明，我们的方法始终优于现有方法，从而产生更准确和可靠的类分配。实验结果突出了我们的方法通过减少类别不确定性和提高整体分类性能来增强 MVRL 的有效性。这种方法不仅具有很强的可解释性，而且还增强了模型学习多视图一致信息的能力。