Interpretable Multi-View Clustering Using Anchor Graph Tensor Factorization

The core message of this paper is to propose an interpretable multi-view clustering model based on anchor graph tensor factorization (AGTF). The approach extends non-negative matrix factorization (NMF) to operate on third-order tensors, preserving more intrinsic spatial structure information across different views. It also utilizes the tensor Schatten p-norm to impose a low-rank constraint on the indicator tensor, effectively capturing the complementary information between views.