Trusted Mamba Contrastive Network for Multi-View Clustering

TL;DR

This paper introduces the Trusted Mamba Contrastive Network (TMCN), a novel multi-view clustering approach that improves fusion reliability by selectively integrating views and aligning representations to enhance clustering accuracy.

Contribution

The paper proposes a new trusted fusion mechanism and an average-similarity contrastive learning module for more accurate multi-view clustering, addressing untrusted fusion issues in prior methods.

Findings

Achieves state-of-the-art results on multi-view clustering benchmarks.

Effectively handles noise and redundant information in views.

Improves view fusion reliability through selective mechanisms.

Abstract

Multi-view clustering can partition data samples into their categories by learning a consensus representation in an unsupervised way and has received more and more attention in recent years. However, there is an untrusted fusion problem. The reasons for this problem are as follows: 1) The current methods ignore the presence of noise or redundant information in the view; 2) The similarity of contrastive learning comes from the same sample rather than the same cluster in deep multi-view clustering. It causes multi-view fusion in the wrong direction. This paper proposes a novel multi-view clustering network to address this problem, termed as Trusted Mamba Contrastive Network (TMCN). Specifically, we present a new Trusted Mamba Fusion Network (TMFN), which achieves a trusted fusion of multi-view data through a selective mechanism. Moreover, we align the fused representation and the view-specific representation using the Average-similarity Contrastive Learning (AsCL) module. AsCL increases the similarity of view presentation from the same cluster, not merely from the same sample. Extensive experiments show that the proposed method achieves state-of-the-art results in deep multi-view clustering tasks. The source code is available at https://github.com/HackerHyper/TMCN.