Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees

ArXi:2310.02543v2 Announce Type: replace We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limitations: (1) they are often task-specific and lack generality or systematic formulation; (2) they typically treat graphs as static structures, ignoring their inherent dynamism in tensor-based settings; (3) they lack theoretical guarantees on statistical and computational complexity. To address these issues, we.