Multi-view clustering endeavors to effectively uncover consistent clustering patterns across multiple data sources or feature spaces. This field grapples with two key challenges: (1) the effective integration and utilization of consistency and complementarity information from diverse view spaces, and (2) the capturing of structural correlations between data samples in the multi-view context. To address these challenges, this paper proposes the Multi-view contrAstive clustering with Graph Aggregation and confidence enhancement (MAGA) algorithm. Specifically, we employ a deep autoencoder network to learn embedded features for each independent view. To harness consistency and complementarity information, we introduce the Simple Cross-view Spectral Graph Aggregation module. This module utilizes graph convolutional layers to generate view-specific graph embeddings and subsequently aggregates these embeddings from different views into a unified feature space using a cross-view self-attention mechanism. To capture both inter-view and intra-view structural correlations among different samples, we propose a dual representation contrastive learning mechanism, which operates concurrently at both the instance and feature levels. Additionally, we introduce the maximizing cluster assignment confidence mechanism to obtain more compact clustering assignments. As a result, MAGA outperforms 20 competitive methods across nine benchmark datasets, showcasing its superior performance. Code: .

中文翻译：

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通过集成图聚合和置信度增强进行多视图对比聚类

多视图聚类致力于有效地发现跨多个数据源或特征空间的一致聚类模式。该领域面临两个关键挑战：（1）有效集成和利用来自不同视图空间的一致性和互补性信息，以及（2）捕获多视图上下文中数据样本之间的结构相关性。为了解决这些挑战，本文提出了具有图聚合和置信度增强（MAGA）算法的多视图对比聚类。具体来说，我们采用深度自动编码器网络来学习每个独立视图的嵌入特征。为了利用一致性和互补性信息，我们引入了简单跨视图谱图聚合模块。该模块利用图卷积层生成特定于视图的图嵌入，然后使用跨视图自注意力机制将来自不同视图的这些嵌入聚合到统一的特征空间中。为了捕获不同样本之间的视图间和视图内结构相关性，我们提出了一种双重表示对比学习机制，该机制在实例和特征级别上同时运行。此外，我们引入了最大化聚类分配置信度机制来获得更紧凑的聚类分配。结果，MAGA 在 9 个基准数据集上优于 20 种竞争方法，展示了其卓越的性能。代码： 。