Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
Researchers have introduced Wedge Sampling, a novel non-adaptive sampling scheme designed for efficient low-rank tensor completion. This new method utilizes structured length-two patterns, known as wedges, within a bipartite sampling graph to strengthen spectral signals. The approach promises polynomial-time algorithms capable of achieving recovery with nearly linear sample complexity, significantly improving upon traditional uniform sampling methods. AI