Low Rank Tensor Completion via Partial Sum Minimization of Singular Values

DOI

10.2991/icacie-16.2016.4How to use a DOI?

Keywords

tensor completion, matrix completion, nuclear norm minimization, alternating direction method of multipliers

Abstract

In this paper, we investigate the low-rank tensor completion problem, in which we wish to estimate missing values of tensors from incomplete samples its entries. In real world, the low-rank tensor can be seen everywhere and the exact rank of it is often known. Based on the fact that singular values before the target rank does not affect rank minimization of tensors, we propose low rank tensor completion via partial sum minimization of singular values algorithm(PSSV-LRTC). Some experiments are performed on both synthetic data and real applications; all results show that our algorithm has a higher precision and convergence rate than previous work.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Feng Zhang
AU  - Jianjun Wang
AU  - Jia Jing
PY  - 2016/10
DA  - 2016/10
TI  - Low Rank Tensor Completion via Partial Sum Minimization of Singular Values
BT  - Proceedings of the 2016 International Conference on Automatic Control and Information Engineering
PB  - Atlantis Press
SP  - 16
EP  - 19
SN  - 2352-5401
UR  - https://doi.org/10.2991/icacie-16.2016.4
DO  - 10.2991/icacie-16.2016.4
ID  - Zhang2016/10
ER  -