@inproceedings{d3f8c98492a3478eb79054af3f28a5e7,
title = "Robust Low-Rank Tensor Recovery from Quantized and Corrupted Measurements",
abstract = "Real-world datasets are commonly represented by higher-order tensors, and easily suffer from quantized and corrupted errors. This paper, for the first time, studies the tensor recovery from quantized and corrupted measurements. A maximum likelihood approach under the exact low-Tucker- rank constraint is proposed to estimate the actual tensor. We provide both an upper bound and lower bound of the recovery error, and the theorems indicate that our method is order-wise optimal when the rank of the tensor is small. We also show that the error decays in the same order as the state-of-the-art method when there is no corruption. An efficient proximal gradient-based solver is proposed to recover the tensor. Experiments on both synthetic data and a public video dataset validate the effectiveness of our method.",
keywords = "Tucker rank, higher-order tensor, low-rank, quantization, robust tensor recovery",
author = "Ren Wang and Tianqi Chen and Zhe Xu and Pengzhi Gao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 ; Conference date: 31-10-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/IEEECONF53345.2021.9723252",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1656--1660",
editor = "Matthews, {Michael B.}",
booktitle = "55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021",
}