MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network

TL;DR

MCFCN introduces an end-to-end multi-view clustering approach that learns a consensus graph and representations by integrating view features and topological structures, enhancing clustering accuracy.

Contribution

The paper proposes a novel Fusion-Consensus Graph Convolutional Network that effectively preserves cross-view topological consistency and improves clustering performance through a unified graph structure and alignment losses.

Findings

Achieves state-of-the-art results on eight benchmark datasets.

Effectively preserves cross-view topological structures.

Demonstrates significant improvements over existing multi-view clustering methods.

Abstract

Existing Multi-view Clustering (MVC) methods based on subspace learning focus on consensus representation learning while neglecting the inherent topological structure of data. Despite the integration of Graph Neural Networks (GNNs) into MVC, their input graph structures remain susceptible to noise interference. Methods based on Multi-view Graph Refinement (MGRC) also have limitations such as insufficient consideration of cross-view consistency, difficulty in handling hard-to-distinguish samples in the feature space, and disjointed optimization processes caused by graph construction algorithms. To address these issues, a Multi-View Clustering method via a Fusion-Consensus Graph Convolutional Network (MCFCN) is proposed. The network learns the consensus graph of multi-view data in an end-to-end manner and learns effective consensus representations through a view feature fusion model and a Unified Graph Structure Adapter (UGA). It designs Similarity Matrix Alignment Loss (SMAL) and Feature Representation Alignment Loss (FRAL). With the guidance of consensus, it optimizes view-specific graphs, preserves cross-view topological consistency, promotes the construction of intra-class edges, and realizes effective consensus representation learning with the help of GCN to improve clustering performance. MCFCN demonstrates state-of-the-art performance on eight multi-view benchmark datasets, and its effectiveness is verified by extensive qualitative and quantitative implementations. The code will be provided at https://github.com/texttao/MCFCN.