CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional
  Network for Clustering
  With the powerful learning ability of deep convolutional networks, deep
clustering methods can extract the most discriminative information from
individual data and produce more satisfactory clustering results. However,
existing deep clustering methods usually ignore the relationship between the
data. Fortunately, the graph convolutional network can handle such
relationship, opening up a new research direction for deep clustering. In this
paper, we propose a cross-attention based deep clustering framework, named
Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN),
which contains four main modules: the cross-attention fusion module which
innovatively concatenates the Content Auto-encoder module (CAE) relating to the
individual data and Graph Convolutional Auto-encoder module (GAE) relating to
the relationship between the data in a layer-by-layer manner, and the
self-supervised model that highlights the discriminative information for
clustering tasks. While the cross-attention fusion module fuses two kinds of
heterogeneous representation, the CAE module supplements the content
information for the GAE module, which avoids the over-smoothing problem of GCN.
In the GAE module, two novel loss functions are proposed that reconstruct the
content and relationship between the data, respectively. Finally, the
self-supervised module constrains the distributions of the middle layer
representations of CAE and GAE to be consistent. Experimental results on
different types of datasets prove the superiority and robustness of the
proposed CaEGCN.
