Incomplete multi-view clustering based on hypergraph

The graph-based incomplete multi-view clustering aims at integrating information from multiple views and utilizes graph models to capture the global and local structure of the data for reconstructing missing data, which is suitable for processing complex data. However, ordinary graph learning methods usually only consider pairwise relationships between data points and cannot unearth higher-order relationships latent in the data. And existing graph clustering methods often divide the process of learning the representations and the clustering process into two separate steps, which may lead to unsatisfactory clustering results. Besides, they also tend to consider only intra-view similarity structures and overlook inter-view ones. To this end, this paper introduces an innovative one-step incomplete multi-view clustering based on hypergraph (IMVC_HG). Specifically, we use a hypergraph to reconstruct missing views, which can better explore the local structure and higher-order information between sample points. Moreover, we use non-negative matrix factorization with orthogonality constraints to equate K-means, which eliminates post-processing operations and avoids the problem of suboptimal results caused by the two-step approach. In addition, the tensor Schatten $p$-norm is used to better capture the complementary information and low-rank structure between the cluster label matrices of multiple views. Numerous experiments verify the superiority of IMVC_HG.