解决多视图聚类中融合不靠谱的问题，提升聚类准确率。
Trusted Mamba Contrastive Network for Multi-View Clustering
- 用选择性机制筛选可信视图，实现可靠融合
- 新对比学习让同簇样本更相似，而非仅同样本
- 适合处理含噪声或冗余信息的多源数据
多视图聚类通过无监督学习共识表示对数据样本进行分类，近年来受到广泛关注。然而，现有方法存在不可信融合问题：一是忽略视图中的噪声或冗余信息；二是对比学习依赖相同样本而非相同簇，导致融合方向错误。本文提出可信马尔可夫对比网络（TMCN），包含受信任的马尔可夫融合网络（TMFN），通过选择性机制实现多视图数据的可信融合；同时引入平均相似性对比学习（AsCL）模块，对齐融合表示与视图特异性表示，使同一簇样本的表示更相似，而非仅同一样本。大量实验表明，该方法在深度多视图聚类任务中达到当前最优性能。代码已公开于 https://github.com/HackerHyper/TMCN。
原文摘要 · Abstract (English)
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.
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