Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization

Incomplete multi-view clustering has attracted attention due to its ability to deal with clustering problems with incomplete information. However, most existing methods either ignore the local structure of the data or fail to consider the importance of different views. In addition, some methods based on mean filling may easily introduce useless information when the data has a large missing rate. To address these issues, this paper proposes an incomplete multi-view clustering algorithm based on graph regularized low-rank representations without using filling method. Specifically, we combine a distance regularization term and low-rank representation-based non-negativity constraints to directly learn graphs with global and local data structures from raw data. Furthermore, we introduce a novel weighted fusion mechanism in the model to learn a consistent representation of all views, which effectively avoids bad views from affecting the quality of the final fused consensus graph. Experimental results on six incomplete multi-view datasets demonstrate that our proposed method achieves the best performance compared with the existing state-of-the-art methods.