标题： 基于局部和全局结构保持的高效多视图图聚类 显示英文标题

标题： Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

摘要： 基于锚点的多视图图聚类（AMVGC）由于其高效性和能够捕捉多个视图之间的互补结构信息的能力而受到广泛关注。直观上，高质量的锚点图在AMVGC的成功中起着关键作用。然而，现有的AMVGC方法仅考虑单结构信息，即局部或全局结构，这为学习任务提供了不足的信息。具体来说，过于分散的全局结构导致学习到的锚点无法很好地描述聚类划分。相反，不恰当的相似性度量会导致局部结构中的锚点分配可能不准确，最终导致次优的聚类性能。为了解决这个问题，我们提出了一种新的基于锚点的多视图图聚类框架，称为保留局部和全局结构的高效多视图图聚类（EMVGC-LG）。具体来说，设计了一个具有理论保证的统一框架来捕捉局部和全局信息。此外，EMVGC-LG联合优化锚点构建和图学习以提高聚类质量。另外，EMVGC-LG继承了现有AMVGC方法对样本数量的线性复杂度，这在时间上是经济的，并且随着数据规模的扩大表现出良好的可扩展性。大量实验证明了我们所提出方法的有效性和效率。 显示英文摘要

摘要： Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.