Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations
This addresses tensor estimation challenges in applications like recommendation systems and neuroimaging, offering an efficient algorithm with theoretical guarantees, though it is incremental as it builds on existing smooth tensor models.
The paper tackles the problem of structured tensor denoising with unknown permutations, showing that a constrained least-squares estimator achieves minimax error bounds and revealing a phase transition where a polynomial of degree up to $(m-2)(m+1)/2$ is sufficient for optimal recovery of order-$m$ tensors.
We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation system, neuroimaging, community detection, and multiway comparison applications. Here, we develop a general family of smooth tensor models up to arbitrary index permutations; the model incorporates the popular tensor block models and Lipschitz hypergraphon models as special cases. We show that a constrained least-squares estimator in the block-wise polynomial family achieves the minimax error bound. A phase transition phenomenon is revealed with respect to the smoothness threshold needed for optimal recovery. In particular, we find that a polynomial of degree up to $(m-2)(m+1)/2$ is sufficient for accurate recovery of order-$m$ tensors, whereas higher degree exhibits no further benefits. This phenomenon reveals the intrinsic distinction for smooth tensor estimation problems with and without unknown permutations. Furthermore, we provide an efficient polynomial-time Borda count algorithm that provably achieves optimal rate under monotonicity assumptions. The efficacy of our procedure is demonstrated through both simulations and Chicago crime data analysis.