  Multi-view clustering is an important research topic due to its capability to
utilize complementary information from multiple views. However, there are few
methods to consider the negative impact caused by certain views with unclear
clustering structures, resulting in poor multi-view clustering performance. To
address this drawback, we propose self-supervised discriminative feature
learning for multi-view clustering (SDMVC). Concretely, deep autoencoders are
applied to learn embedded features for each view independently. To leverage the
multi-view complementary information, we concatenate all views' embedded
features to form the global features, which can overcome the negative impact of
some views' unclear clustering structures. In a self-supervised manner,
pseudo-labels are obtained to build a unified target distribution to perform
multi-view discriminative feature learning. During this process, global
discriminative information can be mined to supervise all views to learn more
discriminative features, which in turn are used to update the target
distribution. Besides, this unified target distribution can make SDMVC learn
consistent cluster assignments, which accomplishes the clustering consistency
of multiple views while preserving their features' diversity. Experiments on
various types of multi-view datasets show that SDMVC achieves state-of-the-art
performance.
