Graph-based multi-view clustering has achieved better performance than most
non-graph approaches. However, in many real-world scenarios, the graph
structure of data is not given or the quality of initial graph is poor.
Additionally, existing methods largely neglect the high-order neighborhood
information that characterizes complex intrinsic interactions. To tackle these
problems, we introduce an approach called high-order multi-view clustering
(HMvC) to explore the topology structure information of generic data. Firstly,
graph filtering is applied to encode structure information, which unifies the
processing of attributed graph data and non-graph data in a single framework.
Secondly, up to infinity-order intrinsic relationships are exploited to enrich
the learned graph. Thirdly, to explore the consistent and complementary
information of various views, an adaptive graph fusion mechanism is proposed to
achieve a consensus graph. Comprehensive experimental results on both non-graph
and attributed graph data show the superior performance of our method with
respect to various state-of-the-art techniques, including some deep learning
methods.

提出了一种名为高阶多视角聚类 (HMvC) 的方法，该方法采用图过滤来编码结构信息，扩充了学习到的图，利用自适应图融合机制来实现一致性的分布式图。实验结果显示，在非图和有属性图数据方面，我们的方法在各种现有技术，包括一些深度学习方法方面都表现出优越性能。