High-order Multi-view Clustering for Generic Data

TL;DR

This paper introduces high-order multi-view clustering (HMvC), a novel method that encodes structure information and exploits high-order relationships to improve clustering performance on generic data, regardless of initial graph quality.

Contribution

The paper proposes a unified framework using graph filtering and high-order relationship exploitation, along with an adaptive graph fusion mechanism, to enhance multi-view clustering for both graph and non-graph data.

Findings

HMvC outperforms state-of-the-art methods on various datasets.

The approach effectively encodes structure information for non-graph data.

High-order relationships significantly improve clustering accuracy.

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 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.