Incomplete multi-view clustering based on hypergraph

Jin Chen, Huafu Xu, Jingjing Xue, Quanxue Gao, Cheng Deng, Ziyu Lv

Abstract: Highlights•Propose a cohesive model to solve missing data, learn representation and clustering.•A hypergraph is used to explore the data structure to reconstruct the missing parts.•Non-negative matrix factorization to equate K-means for clustering in one step.•Tensor Schatten p-norm captures the complementary information in different views.