A Novel Regularized Model for Third-Order Tensor Completion

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yi Yang
  • Lixin Han
  • Yuanzhen Liu
  • Jun Zhu
  • Hong Yan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3473-3483
Journal / PublicationIEEE Transactions on Signal Processing
Volume69
Online published3 Jun 2021
Publication statusPublished - 2021

Abstract

Inspired by the accuracy and efficiency of the ϒ-norm of a matrix, which is closer to the original rank minimization problem than nuclear norm minimization (NNM), we generalize the Υ-norm of a matrix to tensors and propose a new tensor completion approach within the tensor singular value decomposition (t-svd) framework. An efficient algorithm, which combines the augmented Lagrange multiplier and closed-resolution of a cubic equation, was developed to solve the associated nonconvex tensor multi-rank minimization problem. Experimental results show that the proposed approach has an advantage over current state of the art algorithms in recovery accuracy.

Research Area(s)

  • Approximation algorithms, Convex functions, Mathematical model, Matrix decomposition, Minimization, Signal processing algorithms, tensor completion, tensor multi-rank approximation, tensor ϒ nuclear norm, tensor singular value decomposition (t-svd), Tensors

Citation Format(s)

A Novel Regularized Model for Third-Order Tensor Completion. / Yang, Yi; Han, Lixin; Liu, Yuanzhen; Zhu, Jun; Yan, Hong.

In: IEEE Transactions on Signal Processing, Vol. 69, 2021, p. 3473-3483.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review