Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness-Reference-Cited by-同舟云学术

Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness

Author:

Adali Tulay¹,Kantar Furkan²,Akhonda Mohammad Abu Baker Siddique²,Strother Stephen³,Calhoun Vince D.⁴,Acar Evrim⁵

Affiliation:

1. Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, Maryland, USA

2. Machine Learning for Signal Processing Lab, University of Maryland, Baltimore, Maryland, USA

3. Rotman Research Institute, Toronto, Canada

4. Tri-Institutional Center for Translational Research, Neuroimaging and Data Science, Atlanta, Georgia, USA

5. Chief Research Scientist, Simula Metropolitan Center for Digital Engineering, Oslo, Norway

Funder

NSF-NCS

Research Council of Norway

National Institute of Health

National Institute of Mental Health

U.S. Department of Health and Human Services

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

Reference76 articles.

5. Learning from binary multiway data: Probabilistic tensor decomposition and its statistical optimality;wang;J Mach Learn Res,2020

Cited by 19 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

5. Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14