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。

对比聚类分配的深度多视图聚类

Deep Multiview Clustering by Contrasting Cluster Assignments

In this work, we propose a Cross-view Contrastive Learning framework for
unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging
multi-view complementary supervision signal. CrosSCLR consists of both
single-view contrastive learning (SkeletonCLR) and cross-view consistent
knowledge mining (CVC-KM) modules, integrated in a collaborative learning
manner. It is noted that CVC-KM works in such a way that high-confidence
positive/negative samples and their distributions are exchanged among views
according to their embedding similarity, ensuring cross-view consistency in
terms of contrastive context, i.e., similar distributions. Extensive
experiments show that CrosSCLR achieves remarkable action recognition results
on NTU-60 and NTU-120 datasets under unsupervised settings, with observed
higher-quality action representations. Our code is available at
this https URL

本研究提出了一种使用多视角补充监督信号的交叉视图对比学习框架，该框架涵盖了单视图对比学习和交叉视图一致性知识挖掘模块，能够在无监督设置下实现 NTU-60 和 NTU-120 数据集上卓越的动作识别结果。