One-step graph-based incomplete multi-view clustering

Existing graph-based incomplete multi-view clustering methods mainly adopt the three-step strategy, i.e., graph completion, graph fusion (consensus representation learning) and subsequent k-means clustering. Such three-step schemes inevitably seek sub-optimal clustering results due to information loss. Besides, existing methods for incomplete multi-view clustering tasks focus on inferring the missing instances using global complementary information without considering the local structure of data. In addition, their weight allocation strategies for views are mostly static, the model cannot adaptively select the informative views during the process of training. To solve these issues, we propose a novel one-step graph-based incomplete multi-view clustering (OGIMC) method, which introduces the strategy of local structure preservation and adaptive weights into the model. Furthermore, a rank constraint imposed on the Laplacian matrix of the fused graph integrates the separate objectives into a unified training framework. Extensive experimental results demonstrated that OGIMC outperforms state-of-the-art baselines remarkably.