Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches both on eight benchmark MvC datasets and two spatially resolved transcriptomics datasets.

翻译：多视图聚类（MvC）旨在整合来自不同视图的信息，以增强模型捕捉底层数据结构的能力。MvC中广泛使用的联合训练范式可能无法充分利用多视图信息，这是因为对所有视图采用统一学习目标会导致视图特定特征的不平衡和欠优化。例如，具有更多判别性信息的特定视图可能在联合训练范式中主导学习过程，导致其他视图欠优化。为缓解此问题，我们首先从每个视图特定特征提取器的梯度下降角度分析了多视图聚类联合训练范式中的不平衡现象。随后，我们提出了一种新颖的平衡多视图聚类（BMvC）方法，该方法引入了一种视图特定对比正则化（VCR）来调节每个视图的优化。具体而言，VCR将从联合特征和视图特定特征中捕获的样本相似性保留到与视图特定特征对应的聚类分布中，以增强视图特定特征提取器的学习过程。此外，我们提供了理论分析以说明VCR自适应地调节用于更新视图特定特征提取器参数的梯度幅度，从而实现平衡的多视图学习过程。通过这种方式，BMvC在利用视图特定模式和探索视图不变模式之间实现了更好的权衡，从而为聚类任务充分学习多视图信息。最后，我们在八个基准MvC数据集和两个空间分辨转录组学数据集上进行了一系列实验，验证了所提方法相较于最先进方法的优越性。