Incomplete multi-view clustering is a challenging and non-trivial task to
provide effective data analysis for large amounts of unlabeled data in the real
world. All incomplete multi-view clustering methods need to address the problem
of how to reduce the impact of missing views. To address this issue, we propose
diffusion completion to recover the missing views integrated into an incomplete
multi-view clustering framework. Based on the observable views information, the
diffusion model is used to recover the missing views, and then the consistency
information of the multi-view data is learned by contrastive learning to
improve the performance of multi-view clustering. To the best of our knowledge,
this may be the first work to incorporate diffusion models into an incomplete
multi-view clustering framework. Experimental results show that the proposed
method performs well in recovering the missing views while achieving superior
clustering performance compared to state-of-the-art methods.

本文提出了一种基于扩散模型和对比学习的不完全多视角聚类框架，用于解决大量未标记数据的有效数据分析，试图通过恢复缺失视角提高多视角聚类性能。

基于扩散补全的不完整多视角聚类

Incomplete Multi-view Clustering via Diffusion Completion

Learning over multi-view data is a challenging problem with strong practical
applications. Most related studies focus on the classification point of view
and assume that all the views are available at any time. We consider an
extension of this framework in two directions. First, based on the BiGAN model,
the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from
multi-view inputs. Second, it can deal with missing views and is able to update
its prediction when additional views are provided. We illustrate these
properties on a set of experiments over different datasets.

使用 BiGAN 模型，提出了一种多视图 BiGAN（MV-BiGAN）模型，可以从多个视图输入中执行密度估计，并且可以处理缺失视图并在提供额外视图时更新其预测。