Multiview clustering (MVC) aims to reveal the underlying structure of
multiview data by categorizing data samples into clusters. Deep learning-based
methods exhibit strong feature learning capabilities on large-scale datasets.
For most existing deep MVC methods, exploring the invariant representations of
multiple views is still an intractable problem. In this paper, we propose a
cross-view contrastive learning (CVCL) method that learns view-invariant
representations and produces clustering results by contrasting the cluster
assignments among multiple views. Specifically, we first employ deep
autoencoders to extract view-dependent features in the pretraining stage. Then,
a cluster-level CVCL strategy is presented to explore consistent semantic label
information among the multiple views in the fine-tuning stage. Thus, the
proposed CVCL method is able to produce more discriminative cluster assignments
by virtue of this learning strategy. Moreover, we provide a theoretical
analysis of soft cluster assignment alignment. Extensive experimental results
obtained on several datasets demonstrate that the proposed CVCL method
outperforms several state-of-the-art approaches.

本文提出了一种跨视角对比学习（CVCL）方法，该方法利用深度自编码器将视角相关特征提取出来，通过对多个视角下的聚类结果对比来学习视角不变性表示，并利用聚类级别的 CVCL 策略在微调阶段来探索多个视图之间的一致语义标签信息，从而能够产生更有区分度的聚类结果。实验结果表明该方法优于现有的更先进的方法。