  In recent years, incomplete multi-view clustering, which studies the
challenging multi-view clustering problem on missing views, has received
growing research interests. Although a series of methods have been proposed to
address this issue, the following problems still exist: 1) Almost all of the
existing methods are based on shallow models, which is difficult to obtain
discriminative common representations. 2) These methods are generally sensitive
to noise or outliers since the negative samples are treated equally as the
important samples. In this paper, we propose a novel incomplete multi-view
clustering network, called Cognitive Deep Incomplete Multi-view Clustering
Network (CDIMC-net), to address these issues. Specifically, it captures the
high-level features and local structure of each view by incorporating the
view-specific deep encoders and graph embedding strategy into a framework.
Moreover, based on the human cognition, i.e., learning from easy to hard, it
introduces a self-paced strategy to select the most confident samples for model
training, which can reduce the negative influence of outliers. Experimental
results on several incomplete datasets show that CDIMC-net outperforms the
state-of-the-art incomplete multi-view clustering methods.
