Low-Rankness Enhanced Robust Tensor Principal Component Analysis: A Nonlinear Monotonic Function Approach.
- DOI
- 10.1109/tnnls.2026.3733136
- Published
- 2026 Sep 22
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3733136,
title = {Low-Rankness Enhanced Robust Tensor Principal Component Analysis: A Nonlinear Monotonic Function Approach.},
author = {Zhang W and Song Y and Ding D and Tian F},
year = {2026},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2026.3733136},
url = {https://doi.org/10.1109/tnnls.2026.3733136}
}RIS
TY - JOUR TI - Low-Rankness Enhanced Robust Tensor Principal Component Analysis: A Nonlinear Monotonic Function Approach. AU - Zhang W AU - Song Y AU - Ding D AU - Tian F PY - 2026 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2026.3733136 UR - https://doi.org/10.1109/tnnls.2026.3733136 ER -
APA
W, Z., Y, S., D, D., & F, T. (2026). Low-Rankness Enhanced Robust Tensor Principal Component Analysis: A Nonlinear Monotonic Function Approach.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2026.3733136
Source records
- pubmed · retrieved 2026-09-25T00:23:16.448Z