1 research outputs found
Generative Partial Multi-View Clustering
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.Comment: This paper is an extension to our previous work: "Wang Q, Ding Z, Tao
Z, et al. Partial multi-view clustering via consistent GAN[C]//2018 IEEE
International Conference on Data Mining (ICDM). IEEE, 2018: 1290-1295.