Low-Rank Hankel Tensor Completion for Traffic Speed Estimation

Estimating Missing Traffic Volume Using Low Multilinear Rank Tensor Completion

Efficient tensor completion: Low-rank tensor train

This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global information of tensors thanks to its construction by a wellbalanced matricization scheme. Two algorithms are proposed to solve the corresponding tensor completion p...

Interpolation using Hankel tensor completion

We present a novel multidimensional seismic trace interpolator that works on constant-frequency slices. It performs completion on Hankel tensors whose order is twice the number of spatial dimensions. Completion is done by fitting a PARAFAC model using an Alternating Least Squares algorithm. The new interpolator runs quickly and can better handle large gaps and high sparsity than existing comple...

Cross: Efficient Low-rank Tensor Completion

The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set of uniformly randomly measured entries, and the required number of measurements to achieve recovery is not guaranteed to be optimal. In addition, the implementation of some previous methods are NP-hard. In this artic...

Low-Rank Tensor Completion by Riemannian Optimization∗

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. More specifically, a variant of the nonlinear conjugate gradient method is developed. Paying particular attention to the efficient implementation, ou...