Robust Low-Rank Tensor Decomposition with the L <sub>2</sub> Criterion-Reference-Cited by-同舟云学术

Robust Low-Rank Tensor Decomposition with the L ₂ Criterion

Published:2023-05-22 Issue:4 Volume:65 Page:537-552
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Heng Qiang¹^ORCID,Chi Eric C.²^ORCID,Liu Yufeng³^ORCID

Affiliation:

1. Department of Statistics, North Carolina State University, Raleigh, NC

2. Department of Statistics, Rice University, Houston, TX

3. Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC

Funder

National Institutes of Health

National Institute of General Medical Sciences

National Science Foundation

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2023.2200541

Reference64 articles.

1. A scalable optimization approach for fitting canonical tensor decompositions

2. Scalable tensor factorizations for incomplete data

3. Anandkumar, A., Jain, P., Shi, Y., and Niranjan, U. N. (2016), “Tensor vs. Matrix Methods: Robust Tensor Decomposition Under Block Sparse Perturbations,” in Artificial Intelligence and Statistics, PMLR, pp. 268–276.

4. Algorithm 862

5. Efficient MATLAB Computations with Sparse and Factored Tensors

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MacroPARAFAC for handling rowwise and cellwise outliers in incomplete multiway data;Chemometrics and Intelligent Laboratory Systems;2024-08