A generalizable framework for low-rank tensor completion with numerical priors-Reference-Cited by-同舟云学术

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A generalizable framework for low-rank tensor completion with numerical priors

Published:2024-11 Issue: Volume:155 Page:110678
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Yuan Shiran^ORCID,Huang Kaizhu^ORCID

Funder

Duke Kunshan University

Publisher

Elsevier BV

Reference55 articles.

1. Low rank tensor completion for multiway visual data;Long;Signal Process.,2019

2. Tensor methods for low-level vision;Yokota,2022

3. Super resolution for multi-sources image stream data using smooth and sparse tensor completion and its applications in data acquisition of additive manufacturing;Shen;Technometrics,2022

4. Tensor completion algorithms in big data analytics;Song;ACM Trans. Knowl. Discov. Data (TKDD),2019

5. Anomaly detection of hyperspectral image via tensor completion;Wang;IEEE Geosci. Remote Sens. Lett.,2020

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