Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners. Multiple views provide more information than single views and thus existing MVC methods can achieve satisfactory performance. However, their performance might seriously degenerate when the views are noisy in practical multi-view scenarios. In this paper, we first formally investigate the drawback of noisy views and then propose a theoretically grounded deep MVC method (namely MVCAN) to address this issue. Specifically, we propose a novel MVC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore, a two-level multi-view iterative optimization is designed to generate robust learning targets for refining individual views' representation learning. Theoretical analysis reveals that MVCAN works by achieving the multi-view consistency, complementarity, and noise robustness. Finally, experiments on extensive public datasets demonstrate that MVCAN outperforms state-of-the-art methods and is robust against the existence of noisy views.

翻译：多视角聚类旨在以自监督方式探索多视角数据中的类别结构。多视角比单视角提供更多信息，因此现有MVC方法能够取得令人满意的性能。然而，在实际多视角场景中，当视角存在噪声时，其性能可能会严重退化。本文首先系统探究了噪声视角的缺陷，随后提出了一种具有理论依据的深度MVC方法（即MVCAN）以解决该问题。具体而言，我们提出了一种新型MVC目标函数，该函数允许跨多个视角的非共享参数和非一致聚类预测，从而减少噪声视角的副作用。此外，我们设计了一种双层多视角迭代优化机制，用于生成稳健的学习目标以优化各视角的表征学习。理论分析表明，MVCAN通过实现多视角一致性、互补性和噪声鲁棒性发挥作用。最后，在广泛公开数据集上的实验证明，MVCAN优于现有最先进方法，并对噪声视角的存在具有鲁棒性。