  This study investigates the problem of multi-view subspace clustering, the
goal of which is to explore the underlying grouping structure of data collected
from different fields or measurements. Since data do not always comply with the
linear subspace models in many real-world applications, most existing
multi-view subspace clustering methods that based on the shallow linear
subspace models may fail in practice. Furthermore, underlying graph information
of multi-view data is always ignored in most existing multi-view subspace
clustering methods. To address aforementioned limitations, we proposed the
novel multi-view subspace clustering networks with local and global graph
information, termed MSCNLG, in this paper. Specifically, autoencoder networks
are employed on multiple views to achieve latent smooth representations that
are suitable for the linear assumption. Simultaneously, by integrating fused
multi-view graph information into self-expressive layers, the proposed MSCNLG
obtains the common shared multi-view subspace representation, which can be used
to get clustering results by employing the standard spectral clustering
algorithm. As an end-to-end trainable framework, the proposed method fully
investigates the valuable information of multiple views. Comprehensive
experiments on six benchmark datasets validate the effectiveness and
superiority of the proposed MSCNLG.
