  Incomplete multi-view clustering is a hot and emerging topic. It is well
known that unavoidable data incompleteness greatly weakens the effective
information of multi-view data. To date, existing incomplete multi-view
clustering methods usually bypass unavailable views according to prior missing
information, which is considered as a second-best scheme based on evasion.
Other methods that attempt to recover missing information are mostly applicable
to specific two-view datasets. To handle these problems, in this paper, we
propose an information recovery-driven deep incomplete multi-view clustering
network, termed as RecFormer. Concretely, a two-stage autoencoder network with
the self-attention structure is built to synchronously extract high-level
semantic representations of multiple views and recover the missing data.
Besides, we develop a recurrent graph reconstruction mechanism that cleverly
leverages the restored views to promote the representation learning and the
further data reconstruction. Visualization of recovery results are given and
sufficient experimental results confirm that our RecFormer has obvious
advantages over other top methods.
