Regularized high dimension low tubal-rank tensor regression

DOI10.1214/22-EJS2004zbMath1493.62431OpenAlexW4226464447MaRDI QIDQ2137811

George Michailidis, Samrat Roy

Publication date: 11 May 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-1/Regularized-high-dimension-low-tubal-rank-tensor-regression/10.1214/22-EJS2004.full

zbMATH Keywords

tensoralternating minimizationCP decompositiontensor regressiontubal-rank

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Vector and tensor algebra, theory of invariants (15A72)

Uses Software

• gglasso
• rTensor

Cites Work

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• Tensor Decompositions and Applications
• Tensor-Train Decomposition
• Causal inference using invariant prediction: identification and confidence intervals
• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• Factorization strategies for third-order tensors
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Multilinear tensor regression for longitudinal relational data
• Convex multi-task feature learning
• Convex regularization for high-dimensional multiresponse tensor regression
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Robust principal component analysis?
• Rank-Sparsity Incoherence for Matrix Decomposition
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• A Multilinear Singular Value Decomposition
• Exact Tensor Completion Using t-SVD
• Tensor Regression Using Low-Rank and Sparse Tucker Decompositions
• Low-Tubal-Rank Tensor Completion Using Alternating Minimization
• Robust PCA via Outlier Pursuit
• Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
• Tensor Regression with Applications in Neuroimaging Data Analysis
• A fast unified algorithm for solving group-lasso penalize learning problems
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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