  Deep multi-view subspace clustering (DMVSC) has recently attracted increasing
attention due to its promising performance. However, existing DMVSC methods
still have two issues: (1) they mainly focus on using autoencoders to
nonlinearly embed the data, while the embedding may be suboptimal for
clustering because the clustering objective is rarely considered in
autoencoders, and (2) existing methods typically have a quadratic or even cubic
complexity, which makes it challenging to deal with large-scale data. To
address these issues, in this paper we propose a novel deep multi-view subspace
clustering method with anchor graph (DMCAG). To be specific, DMCAG firstly
learns the embedded features for each view independently, which are used to
obtain the subspace representations. To significantly reduce the complexity, we
construct an anchor graph with small size for each view. Then, spectral
clustering is performed on an integrated anchor graph to obtain pseudo-labels.
To overcome the negative impact caused by suboptimal embedded features, we use
pseudo-labels to refine the embedding process to make it more suitable for the
clustering task. Pseudo-labels and embedded features are updated alternately.
Furthermore, we design a strategy to keep the consistency of the labels based
on contrastive learning to enhance the clustering performance. Empirical
studies on real-world datasets show that our method achieves superior
clustering performance over other state-of-the-art methods.
