  With the representation learning capability of the deep learning models, deep
embedded multi-view clustering (MVC) achieves impressive performance in many
scenarios and has become increasingly popular in recent years. Although great
progress has been made in this field, most existing methods merely focus on
learning the latent representations and ignore that learning the latent graph
of nodes also provides available information for the clustering task. To
address this issue, in this paper we propose Deep Embedded Multi-view
Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ),
which utilizes the latent graphs to promote the performance of deep embedded
MVC models from two aspects. Firstly, by learning the latent graphs and feature
representations jointly, the graph convolution network (GCN) technique becomes
available for our model. With the capability of GCN in exploiting the
information from both graphs and features, the clustering performance of our
model is significantly promoted. Secondly, based on the adjacency relations of
nodes shown in the latent graphs, we design a sample-weighting strategy to
alleviate the noisy issue, and further improve the effectiveness and robustness
of the model. Experimental results on different types of real-world multi-view
datasets demonstrate the effectiveness of DMVCJ.
