  Multi-view clustering can explore consistent information from different views
to guide clustering. Most existing works focus on pursuing shallow consistency
in the feature space and integrating the information of multiple views into a
unified representation for clustering. These methods did not fully consider and
explore the consistency in the semantic space. To address this issue, we
proposed a novel Multi-level Consistency Collaborative learning framework
(MCoCo) for multi-view clustering. Specifically, MCoCo jointly learns cluster
assignments of multiple views in feature space and aligns semantic labels of
different views in semantic space by contrastive learning. Further, we designed
a multi-level consistency collaboration strategy, which utilizes the consistent
information of semantic space as a self-supervised signal to collaborate with
the cluster assignments in feature space. Thus, different levels of spaces
collaborate with each other while achieving their own consistency goals, which
makes MCoCo fully mine the consistent information of different views without
fusion. Compared with state-of-the-art methods, extensive experiments
demonstrate the effectiveness and superiority of our method.
