DeepAI

# Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach

We propose a novel tensor completion approach by equating it to a system identification task. The key is to regard the coordinates and values of the known entries as inputs and outputs, respectively. By assuming a tensor train format initialized with low-rank tensor cores, the latter are iteratively identified via a simple alternating linear scheme to reduce residuals. Experiments verify the superiority of the proposed scheme in terms of both speed and accuracy, where a speedup of up to 23× is observed compared to state-of-the-art tensor completion methods at a similar accuracy.

• 14 publications
• 16 publications
• 19 publications
• 26 publications
04/17/2018

### Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains

We propose a new tensor completion method based on tensor trains. The to...
08/03/2017

### Beyond Low Rank: A Data-Adaptive Tensor Completion Method

Low rank tensor representation underpins much of recent progress in tens...
12/11/2019

### Tensor Completion via Gaussian Process Based Initialization

In this paper, we consider the tensor completion problem representing th...
03/17/2020

03/29/2022

### Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion

Existing low-rank tensor completion (LRTC) approaches aim at restoring a...
05/30/2021

### Non-local Patch-based Low-rank Tensor Ring Completion for Visual Data

Tensor completion is the problem of estimating the missing entries of a ...
10/23/2019

### Deterministic tensor completion with hypergraph expanders

We provide a novel analysis of low rank tensor completion based on hyper...