# Trusted Mamba Contrastive Network for Multi-View Clustering

arXiv: https://arxiv.org/abs/2412.16487 · HTML: https://mlsanity.com/paper/2412.16487/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Vision Mamba backbones](https://mlsanity.com/ssm/vision-mamba-backbones/)

## Builds on (most cited first, 14 of 14)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models](https://mlsanity.com/paper/2410.03577/) (2024)
- [Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder](https://mlsanity.com/paper/1802.02305/) (2018)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [Deep Multimodal Subspace Clustering Networks](https://mlsanity.com/paper/1804.06498/) (2018)
- [Deep Embedded Multi-view Clustering with Collaborative Training](https://mlsanity.com/paper/2007.13067/) (2020)
- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (2023)
- [Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios](https://mlsanity.com/paper/2411.02708/) (2024)
- [k-Means Clustering Is Matrix Factorization](https://mlsanity.com/paper/1512.07548/) (2015)
- [Deep Metric Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2304.06358/) (2023)
- [Central Similarity Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2308.13774/) (2023)
- [Adaptive Confidence Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2312.07327/) (2023)

## Built on by (most cited first, 3 of 3)

- [Learning Disentangled Representations for Generalized Multi-view Clustering](https://mlsanity.com/paper/2605.15640/) (2026)
- [Generative Diffusion Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2509.09527/) (2025)
- [THCRL: Trusted Hierarchical Contrastive Representation Learning for Multi-View Clustering](https://mlsanity.com/paper/2512.00368/) (2025)

## Abstract (arXiv)

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.