  Clustering multi-view data has been a fundamental research topic in the
computer vision community. It has been shown that a better accuracy can be
achieved by integrating information of all the views than just using one view
individually. However, the existing methods often struggle with the issues of
dealing with the large-scale datasets and the poor performance in
reconstructing samples. This paper proposes a novel multi-view clustering
method by learning a shared generative latent representation that obeys a
mixture of Gaussian distributions. The motivation is based on the fact that the
multi-view data share a common latent embedding despite the diversity among the
views. Specifically, benefited from the success of the deep generative
learning, the proposed model not only can extract the nonlinear features from
the views, but render a powerful ability in capturing the correlations among
all the views. The extensive experimental results, on several datasets with
different scales, demonstrate that the proposed method outperforms the
state-of-the-art methods under a range of performance criteria.
