Nowadays, with the rapid development of data collection sources and feature
extraction methods, multi-view data are getting easy to obtain and have
received increasing research attention in recent years, among which, multi-view
clustering (MVC) forms a mainstream research direction and is widely used in
data analysis. However, existing MVC methods mainly assume that each sample
appears in all the views, without considering the incomplete view case due to
data corruption, sensor failure, equipment malfunction, etc. In this study, we
design and build a generative partial multi-view clustering model, named as
GP-MVC, to address the incomplete multi-view problem by explicitly generating
the data of missing views. The main idea of GP-MVC lies at two-fold. First,
multi-view encoder networks are trained to learn common low-dimensional
representations, followed by a clustering layer to capture the consistent
cluster structure across multiple views. Second, view-specific generative
adversarial networks are developed to generate the missing data of one view
conditioning on the shared representation given by other views. These two steps
could be promoted mutually, where learning common representations facilitates
data imputation and the generated data could further explores the view
consistency. Moreover, an weighted adaptive fusion scheme is implemented to
exploit the complementary information among different views. Experimental
results on four benchmark datasets are provided to show the effectiveness of
the proposed GP-MVC over the state-of-the-art methods.

本研究提出 GP-MVC 模型，通过生成缺失视图的数据来解决不完整的多视角聚类问题，使用多视角编码器网络和生成对抗网络学习共享表示和生成缺失数据，并利用加权自适应融合方案来利用不同视图的互补信息。通过实验证明，该方法在四个基准数据集上优于现有的多视图聚类方法。