High-order Multi-view Clustering for Generic Data
This addresses the challenge of clustering multi-view data without reliable graphs, which is incremental as it builds on graph-based methods by incorporating high-order information.
The paper tackles the problem of multi-view clustering when graph structures are missing or poor, and high-order neighborhood information is neglected, by introducing HMvC, which achieves superior performance over state-of-the-art techniques on both non-graph and attributed graph data.
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.