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.

翻译：多视图聚类（MVC）旨在通过对数据样本进行聚类来揭示多视图数据的潜在结构。基于深度学习的方法在大规模数据集上展现出强大的特征学习能力。对于现有的大多数深度MVC方法而言，探索多视图的不变表示仍然是一个棘手的问题。本文提出了一种跨视图对比学习（CVCL）方法，通过学习视图不变表示，并通过对比多视图间的聚类分配来生成聚类结果。具体而言，我们首先在预训练阶段使用深度自编码器提取视图相关的特征。随后，在微调阶段提出了一种聚类级别的CVCL策略，以挖掘多视图间一致的语义标签信息。因此，所提出的CVCL方法能够凭借这一学习策略生成更具判别性的聚类分配。此外，我们还对软聚类分配对齐进行了理论分析。在多个数据集上的大量实验结果表明，所提出的CVCL方法优于多种现有最先进方法。