Incomplete Contrastive Multi-View Clustering with High-Confidence
  Guiding
Guoqing Chao Yi Jiang Dianhui Chu
Incomplete multi-view clustering becomes an important research problem, since
multi-view data with missing values are ubiquitous in real-world applications.
Although great efforts have been made for incomplete multi-view clustering,
there are still some challenges: 1) most existing methods didn't make full use
of multi-view information to deal with missing values; 2) most methods just
employ the consistent information within multi-view data but ignore the
complementary information; 3) For the existing incomplete multi-view clustering
methods, incomplete multi-view representation learning and clustering are
treated as independent processes, which leads to performance gap. In this work,
we proposed a novel Incomplete Contrastive Multi-View Clustering method with
high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency
relation transfer plus graph convolutional network to tackle missing values
problem. Secondly, instance-level attention fusion and high-confidence guiding
are proposed to exploit the complementary information while instance-level
contrastive learning for latent representation is designed to employ the
consistent information. Thirdly, an end-to-end framework is proposed to
integrate multi-view missing values handling, multi-view representation
learning and clustering assignment for joint optimization. Experiments compared
with state-of-the-art approaches demonstrated the effectiveness and superiority
of our method. Our code is publicly available at
https://github.com/liunian-Jay/ICMVC.