This paper proposes a novel approach to tensor completion, which recovers missing entries
of data represented by tensors. The approach is based on the tensor train (TT) rank, which is …

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for
multilinear data analysis against outliers, gross corruptions, and missing values and has a …

Abstract We consider the Principal Component Analysis problem for large tensors of
arbitrary order k under a single-spike (or rank-one plus noise) model. On the one hand, we …

Recovering a low-rank tensor from incomplete information is a recurring problem in signal
processing and machine learning. The most popular convex relaxation of this problem …

Short-term traffic prediction plays a critical role in many important applications of intelligent
transportation systems such as traffic congestion control and smart routing, and numerous …

Many problems can be formulated as recovering a low-rank tensor. Although an increasingly
common task, tensor recovery remains a challenging problem because of the delicacy …

We present a novel accelerated primal-dual (APD) method for solving a class of
deterministic and stochastic saddle point problems (SPPs). The basic idea of this algorithm …

An optimal statistical and computational framework for generalized tensor estimation Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 1–29 https://doi.org/10.1214/21-AOS2061 © Institute of …