Mar 19, 2020 · In this paper, we study the tensor completion problem when the sampling pattern is deterministic and possibly non-uniform. We first propose an ...

Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful ...

We first propose an efficient weighted Higher. Order Singular Value Decomposition (HOSVD) algorithm for the recovery of the underlying low- rank tensor from ...

We first propose an efficient weighted Higher Order Singular Value Decomposition (HOSVD) algorithm for the recovery of the underlying low-rank tensor from noisy ...

Dec 9, 2024 · We first propose an efficient weighted Higher Order Singular Value Decomposition (HOSVD) algorithm for the recovery of the underlying low-rank ...

An efficient weighted HOSVD algorithm is proposed for recovery of the underlying low-rank tensor from noisy observations and then derive the error bounds ...

Jul 1, 2021 · We propose a new online algorithm, called TOUCAN, forthe tensor completion problem of imputing missing entriesof a low tubal-rank tensor using ...

We first propose an efficient weighted Higher Order Singular Value Decomposition (HOSVD) algorithm for the recovery of the underlying low-rank tensor from noisy ...

We first propose an efficient weighted Higher Order Singular Value Decomposition (HOSVD) algorithm for the recovery of the underlying low-rank tensor from noisy ...

Mar 24, 2020 · Bibliographic details on HOSVD-Based Algorithm for Weighted Tensor Completion.