Nonconvex Low Tubal Rank Tensor Minimization
In the sparse vector recovery problem, the L<sub>0</sub>-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a nonconvex function on t...
Main Authors: | Yaru Su, Xiaohui Wu, Genggeng Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8913528/ |
Similar Items
-
Anomaly Detection via Tensor Multisubspace Learning and Nonconvex Low-Rank Regularization
by: Sitian Liu, et al.
Published: (2023-01-01) -
Dynamic Magnetic Resonance Imaging via Nonconvex Low-Rank Matrix Approximation
by: Fei Xu, et al.
Published: (2017-01-01) -
Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation
by: Hongyang Lu, et al.
Published: (2016-06-01) -
Tensor recovery from noisy and multi-level quantized measurements
by: Ren Wang, et al.
Published: (2020-09-01) -
Hyperspectral Image Denoising via Correntropy-Based Nonconvex Low-Rank Approximation
by: Peizeng Lin, et al.
Published: (2024-01-01)