GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering

TL;DR

GCFAggMVC introduces a novel multi-view clustering approach that leverages cross-sample and cross-view feature aggregation along with structure-guided contrastive learning to improve clustering accuracy, especially in incomplete data scenarios.

Contribution

The paper proposes a new multi-view clustering network that fully explores sample relationships and aligns view-specific and consensus representations using contrastive learning.

Findings

Achieves state-of-the-art performance on complete multi-view clustering tasks.

Effectively handles incomplete multi-view data clustering.

Demonstrates flexibility by integrating into other frameworks.

Abstract

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary ofsimilar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.