Robust Low-Rank Tensor Recovery from Quantized and Corrupted Measurements

Ren Wang, Tianqi Chen, Zhe Xu, Pengzhi Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1656-1660
Number of pages5
ISBN (Electronic)9781665458283
DOIs
StatePublished - 2021
Externally publishedYes
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period10/31/2111/3/21

Keywords

  • higher-order tensor
  • low-rank
  • quantization
  • robust tensor recovery
  • Tucker rank

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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