Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitations, we propose a structure-guided deep multi-view clustering model. Specifically, we introduce a positive sample selection strategy based on neighborhood relationships, coupled with a corresponding loss function. This strategy constructs multi-view nearest neighbor graphs to dynamically redefine positive sample pairs, enabling the mining of local structural information within multi-view data and enhancing the reliability of positive sample selection. Additionally, we introduce a Gaussian distribution model to uncover latent structural information and introduce a loss function to reduce discrepancies between view embeddings. These two strategies explore multi-view structural information and data distribution from different perspectives, enhancing consistency across views and increasing intra-cluster compactness. Experimental evaluations demonstrate the efficacy of our method, showing significant improvements in clustering performance on multiple benchmark datasets compared to state-of-the-art multi-view clustering approaches.

翻译：深度多视图聚类旨在利用多视图的丰富信息来提升聚类性能。然而，现有的大多数聚类方法往往未能充分挖掘多视图结构信息，也未能探索多视图数据的分布，从而限制了聚类性能。为应对这些局限性，我们提出了一种结构引导的深度多视图聚类模型。具体而言，我们引入了一种基于邻域关系的正样本选择策略，并配以相应的损失函数。该策略通过构建多视图最近邻图来动态重定义正样本对，从而能够挖掘多视图数据内部的局部结构信息，并提升正样本选择的可靠性。此外，我们引入了一个高斯分布模型来揭示潜在的结构信息，并设计了一个损失函数以减少视图嵌入之间的差异。这两种策略从不同角度探索了多视图结构信息与数据分布，增强了视图间的一致性并提高了簇内紧密度。实验评估证明了我们方法的有效性，在多个基准数据集上，与当前最先进的多视图聚类方法相比，我们的方法在聚类性能上取得了显著提升。