面向对比多视图聚类的可靠聚类数估计
Reliable Clustering Number Estimation for Contrastive Multi-View Clustering
把「聚类数 k 未知」当作可学习变量，用簇内/簇间距离构造强化学习奖励来自动推断 k，并用成对表征差异自适应加权视图以缓解对比学习中的表征退化——这两点可直接迁移到视觉表征的无监督聚类与多视图融合流程中。
原文依据 contrastive learning might cause representation degeneration when thecollected multiple views inherently have inconsistent semantic information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近年来，对比多视图聚类取得了显著的性能提升。然而，现有方法仍面临两个关键挑战：（1）依赖预先给定的聚类数 k，而在真实场景中该数值往往未知；（2）当所采集的多个视图本身含有不一致的语义信息时，对比学习可能引发表征退化。为解决上述问题，我们提出了一种新框架——面向对比多视图聚类的可靠聚类数估计（RCNMC）。RCNMC 由语义感知对比学习模块与基于强化学习的聚类数学习模块组成。具体而言，语义感知对比学习模块首先度量成对表征之间的差异，自适应地增强有用的成对视图并削弱不可靠的视图，从而缓解表征退化。基于强化学习的聚类数学习模块以簇内距离与簇间距离作为奖励驱动的策略，以无监督方式推断最优聚类数。两个模块相互补充，使 RCNMC 更适用于真实场景中复杂的多视图聚类任务。在多个基准数据集上的大量实验表明，RCNMC 显著优于现有的最优方法。
英文原文摘要 1414 字符
In recent years, contrastive multi-view clustering has achieved remarkable performance improvements. However, existing methods still face two key challenges: (1) reliance on a predefined number of clusters k, which is often unknown in real-world scenarios; and (2) contrastive learning might cause representation degeneration when thecollected multiple views inherently have inconsistent semantic information . To address these issues, we propose a novel framework--Reliable Clustering Number Estimation for Contrastive Multi-View Clustering (RCNMC). RCNMC consists of a Semantics-Aware Contrastive Learning module and a Reinforcement Learning-based Cluster Number Learning module. Specifically, the Semantics-Aware Contrastive Learning module first measures the discrepancy between pairwise representations and adaptively strengthens useful pairwise views while weakening unreliable ones, thereby alleviating representation degeneration. The Reinforcement Learning-based Cluster Number Learning module infers the optimal number of clusters in an unsupervised manner by using intra-cluster and inter-cluster distances as a reward-driven strategy. The two modules complement each other, making RCNMC more suitable for complex multi-view clustering tasks in real-world scenarios. Extensive experiments on multiple benchmark datasets demonstrate that RCNMC significantly outperforms existing state-of-the-art methods.