Orthogonal random projection for tensor completion

Orthogonal random projection for tensor completion

The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD) operation to calculate the Tucker factors, so...

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Bibliographic Details
Main Authors: Yali Feng, Guoxu Zhou
Format: Article
Language:English
Published: Wiley 2020-08-01
Series:IET Computer Vision
Subjects:
orthogonal random projection
low-rank tensor completion problem
missing data
partially observable data
Tucker decomposition
singular value decomposition operation
Online Access:https://doi.org/10.1049/iet-cvi.2018.5764

Internet

https://doi.org/10.1049/iet-cvi.2018.5764

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