Open AccessA parallel multi‐block alternating direction method of multipliers for tensor completion
Author(s) -
Zhu Hu,
Wang Zhongyang,
Yan Taiyu,
Yu YuFeng,
Deng Lizhen,
Bao BingKun
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12289
Subject(s) - tensor (intrinsic definition) , mathematical optimization , minification , matrix norm , block (permutation group theory) , convergence (economics) , algorithm , computer science , norm (philosophy) , mathematics , dual (grammatical number) , power iteration , iterative method , art , eigenvalues and eigenvectors , physics , geometry , literature , quantum mechanics , political science , pure mathematics , law , economics , economic growth
This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global solution for solving nuclear norm minimization in tensor completion problem with high missing ratio. To tackle this issue, an adaptive tensor completion method based on parallel multi‐block alternating direction method of multipliers (ADMM) algorithm is proposed, it can derive the model from the initial estimate and compute the next estimate from the current solution. The parallel multi‐block ADMM with global convergence is adopted to solve the dual problem, which greatly improves the processing power and reliability of the algorithm.