Deep multi-view clustering, which can obtain complementary information from different views, has received considerable attention in recent years. Although some efforts have been made and achieve decent performances, most of them overlook the structural information and are susceptible to poor quality views, which may seriously restrict the capacity for clustering. To this end, we propose tructural deep ulti-iew lustering with integrated abstraction and detail (SMVC). Specifically, multi-layer perceptrons are used to extract features from specific views, which are then concatenated to form the global features. Besides, a global target distribution is constructed and guides the soft cluster assignments of specific views. In addition to the exploitation of the top-level abstraction, we also design the mining of the underlying details. We construct instance-level contrastive learning using high-order adjacency matrices, which has an equivalent effect to graph attention network and reduces feature redundancy. By integrating the top-level abstraction and underlying detail into a unified framework, our model can jointly optimize the cluster assignments and feature embeddings. Extensive experiments on four benchmark datasets have demonstrated that the proposed SMVC consistently outperforms the state-of-the-art methods.

中文翻译：

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具有集成抽象和细节的结构深度多视图聚类

深度多视图聚类可以从不同视图获取互补信息，近年来受到了相当多的关注。尽管已经做出了一些努力并取得了不错的性能，但大多数都忽略了结构信息并且容易受到质量较差的视图的影响，这可能会严重限制聚类的能力。为此，我们提出了具有集成抽象和细节的结构深层多视图光泽（SMVC）。具体来说，多层感知器用于从特定视图中提取特征，然后将这些特征连接起来形成全局特征。此外，还构建了全局目标分布并指导特定视图的软聚类分配。除了顶层抽象的挖掘之外，我们还设计了底层细节的挖掘。我们使用高阶邻接矩阵构建实例级对比学习，这与图注意网络具有等效的效果并减少特征冗余。通过将顶层抽象和底层细节集成到统一的框架中，我们的模型可以联合优化集群分配和特征嵌入。对四个基准数据集的大量实验表明，所提出的 SMVC 始终优于最先进的方法。