Low-rank, Orthogonally Decomposable Tensor Regression With Application to Visual Stimulus Decoding of fMRI Data

DOI10.1080/10618600.2021.1951741OpenAlexW3181597881MaRDI QIDQ5083366

J. C. Poythress, Jeongyoun Ahn, Cheol-Woo Park

Publication date: 22 June 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2021.1951741

zbMATH Keywords

tensor decompositionlow-rank approximationnuclear normimaging datahigh-dimensional low sample size

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• TensorToolbox

Cites Work

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• Tensor Decompositions and Applications
• Tensor decompositions for learning latent variable models
• L2RM: Low-Rank Linear Regression Models for High-Dimensional Matrix Responses
• On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors
• Non-convex Optimization for Machine Learning
• Regularized Matrix Regression
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Tensor Learning for Regression
• Nonparametric matrix response regression with application to brain imaging data analysis

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