  Most multi-view clustering methods are limited by shallow models without
sound nonlinear information perception capability, or fail to effectively
exploit complementary information hidden in different views. To tackle these
issues, we propose a novel Subspace-Contrastive Multi-View Clustering (SCMC)
approach. Specifically, SCMC utilizes view-specific auto-encoders to map the
original multi-view data into compact features perceiving its nonlinear
structures. Considering the large semantic gap of data from different
modalities, we employ subspace learning to unify the multi-view data into a
joint semantic space, namely the embedded compact features are passed through
multiple self-expression layers to learn the subspace representations,
respectively. In order to enhance the discriminability and efficiently excavate
the complementarity of various subspace representations, we use the contrastive
strategy to maximize the similarity between positive pairs while differentiate
negative pairs. Thus, a weighted fusion scheme is developed to initially learn
a consistent affinity matrix. Furthermore, we employ the graph regularization
to encode the local geometric structure within varying subspaces for further
fine-tuning the appropriate affinities between instances. To demonstrate the
effectiveness of the proposed model, we conduct a large number of comparative
experiments on eight challenge datasets, the experimental results show that
SCMC outperforms existing shallow and deep multi-view clustering methods.
