Multiview clustering (MVC) segregates data samples into meaningful clusters
by synthesizing information across multiple views. Moreover, deep
learning-based methods have demonstrated their strong feature learning
capabilities in MVC scenarios. However, effectively generalizing feature
representations while maintaining consistency is still an intractable problem.
In addition, most existing deep clustering methods based on contrastive
learning overlook the consistency of the clustering representations during the
clustering process. In this paper, we show how the above problems can be
overcome and propose a consistent enhancement-based deep MVC method via
contrastive learning (CCEC). Specifically, semantic connection blocks are
incorporated into a feature representation to preserve the consistent
information among multiple views. Furthermore, the representation process for
clustering is enhanced through spectral clustering, and the consistency across
multiple views is improved. Experiments conducted on five datasets demonstrate
the effectiveness and superiority of our method in comparison with the
state-of-the-art (SOTA) methods. The code for this method can be accessed at
this https URL

通过对比学习构建一致增强的深度多视图聚类方法（CCEC），该方法通过多个视图之间的语义连接块保存一致信息，并通过谱聚类增强聚类过程的一致性。实验证明该方法在五个数据集上相比最先进方法具有更好的效果和优越性。

基于一致性增强的深度多视图对比学习的聚类

Consistency Enhancement-Based Deep Multiview Clustering via Contrastive  Learning

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

Multiview clustering has been extensively studied to take advantage of
multi-source information to improve the clustering performance. In general,
most of the existing works typically compute an n * n affinity graph by some
similarity/distance metrics (e.g. the Euclidean distance) or learned
representations, and explore the pairwise correlations across views. But
unfortunately, a quadratic or even cubic complexity is often needed, bringing
about difficulty in clustering largescale datasets. Some efforts have been made
recently to capture data distribution in multiple views by selecting view-wise
anchor representations with k-means, or by direct matrix factorization on the
original observations. Despite the significant success, few of them have
considered the view-insufficiency issue, implicitly holding the assumption that
each individual view is sufficient to recover the cluster structure. Moreover,
the latent integral space as well as the shared cluster structure from multiple
insufficient views is not able to be simultaneously discovered. In view of
this, we propose an Adaptively-weighted Integral Space for Fast Multiview
Clustering (AIMC) with nearly linear complexity. Specifically, view generation
models are designed to reconstruct the view observations from the latent
integral space with diverse adaptive contributions. Meanwhile, a centroid
representation with orthogonality constraint and cluster partition are
seamlessly constructed to approximate the latent integral space. An alternate
minimizing algorithm is developed to solve the optimization problem, which is
proved to have linear time complexity w.r.t. the sample size. Extensive
experiments conducted on several realworld datasets confirm the superiority of
the proposed AIMC method compared with the state-of-the-art methods.

提出了一种自适应加权积分空间 (AIMC) 方法，用于快速多视图聚类，该方法具有近乎线性时间复杂度，通过视图生成模型和质心表示构造近似的潜在积分空间和聚类划分，通过多个视图发现了潜在整体空间和共享聚类结构，通过实验验证了该方法的卓越性能。