A Novel Regularized Model for Third-Order Tensor Completion
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 3473-3483 |
Journal / Publication | IEEE Transactions on Signal Processing |
Volume | 69 |
Online published | 3 Jun 2021 |
Publication status | Published - 2021 |
Link(s)
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 et al.
In: IEEE Transactions on Signal Processing, Vol. 69, 2021, p. 3473-3483.
In: IEEE Transactions on Signal Processing, Vol. 69, 2021, p. 3473-3483.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review