Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation

Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as...

Full description

Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Shuting Cai, Qilun Luo, Ming Yang, Wen Li, Mingqing Xiao
Format:	Article
Language:	English
Published:	MDPI AG 2019-04-01
Subjects:	tensor robust principal component analysis t-SVD non-convex tensor norm
Online Access:	https://www.mdpi.com/2076-3417/9/7/1411