Bottom-Up Structural Exploration for One-Step Multi-View Graph Clustering

Abstract

In recent years, tensor-based methods have seen considerable success in multi-view clustering. However, the current approach has several limitations: 1) Insufficient exploration of underlying similarity information (i.e. latent representation); 2) Insufficient exploration of  higher-order structure information of both inter-view and intra-view; 3) Treating clustering learning independently from tensor learning and the overall learning framework. To address these issues, we propose a unified framework called Bottom-up Structural Exploration for One-step Multi-view Graph Clustering (BSE_OMGC). Specifically, we first employ an anchor strategy to build similarity graphs, reducing the complexity of graph learning. To deeply represent the underlying similar information of the data and mitigate the influence of noise on similar structures in the original space, BSE_OMGC adaptively separates the noise matrix from the similarity graphs to learn high-quality enhanced graphs. Subsequently, from the bottom up, the enhanced graphs serve as the foundation for constructing high-order tensors. We rotate the constructed tensors and apply the t-TNN to preserve the low-rank properties and to better capture higher-order structure information of both inter-view and intra-view. Finally, we introduce a symmetric non-negative matrix factorization-based graph partitioning technique, which learns non-negative embeddings during dynamic optimization to reveal clustering results. This approach unifies clustering learning within the entire learning framework.  Extensive experiments have confirmed the efficacy of our approach.

Keywords: Multi-view learning, Anchor graph, Low-rank tensor, One-step learning