  This paper study one challenging issue in incomplete multi-view clustering,
where valuable complementary information from other views is always ignored. To
be specific, we propose a framework that effectively balances Complementarity
and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC).
Specifically, we design a dual network of delayed activation, which achieves a
balance of complementarity and consistency among different views. The delayed
activation could enriches the complementarity information that was ignored
during consistency learning. Then, we recover the incomplete information and
enhance the consistency learning by minimizing the conditional entropy and
maximizing the mutual information across different views. This could be the
first theoretical attempt to incorporate delayed activation into incomplete
data recovery and the balance of complementarity and consistency. We have
proved the effectiveness of CoCo-IMC in extensive comparative experiments with
12 state-of-the-art baselines on four publicly available datasets.
