  Exploring the complementary information of multi-view data to improve
clustering effects is a crucial issue in multi-view clustering. In this paper,
we propose a novel model based on information theory termed Informative
Multi-View Clustering (IMVC), which extracts the common and view-specific
information hidden in multi-view data and constructs a clustering-oriented
comprehensive representation. More specifically, we concatenate multiple
features into a unified feature representation, then pass it through a encoder
to retrieve the common representation across views. Simultaneously, the
features of each view are sent to a encoder to produce a compact view-specific
representation, respectively. Thus, we constrain the mutual information between
the common representation and view-specific representations to be minimal for
obtaining multi-level information. Further, the common representation and
view-specific representation are spliced to model the refined representation of
each view, which is fed into a decoder to reconstruct the initial data with
maximizing their mutual information. In order to form a comprehensive
representation, the common representation and all view-specific representations
are concatenated. Furthermore, to accommodate the comprehensive representation
better for the clustering task, we maximize the mutual information between an
instance and its k-nearest neighbors to enhance the intra-cluster aggregation,
thus inducing well separation of different clusters at the overall aspect.
Finally, we conduct extensive experiments on six benchmark datasets, and the
experimental results indicate that the proposed IMVC outperforms other methods.
