Research Area:  Machine Learning

Abstract:

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 long-distance structural 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.

Keywords:  

Author(s) Name:  Erlin Pan , Zhao Kang

Publisher name:  ScienceDirect

DOI:  10.1016/j.inffus.2023.101947

Volume Information:  Volume 100,(2023)

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1566253523002634