Tensor Regression Using Low-Rank and Sparse Tucker Decompositions

DOI10.1137/19M1299335MaRDI QIDQ5037550

Haroon Raja, Waheed U. Bajwa, Talal Ahmed

Publication date: 1 March 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.03725

zbMATH Keywords

linear regressionsparsityTucker decompositionsample complexitytensor regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10) Multidimensional problems (41A63) Uniqueness of best approximation (41A52)

Related Items (2)

Regularized high dimension low tubal-rank tensor regression ⋮ Tensor completion by multi-rank via unitary transformation

Uses Software

• Matlab
• TensorToolbox
• Tensorlab

Cites Work

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• Suprema of Chaos Processes and the Restricted Isometry Property
• Sample Complexity of Dictionary Learning and Other Matrix Factorizations
• Tensor completion and low-n-rank tensor recovery via convex optimization
• Tensor Decomposition for Signal Processing and Machine Learning
• An Introduction to Statistical Learning
• Sparse and Low-Rank Tensor Estimation via Cubic Sketchings
• Sparse Signal Recovery with Exponential-Family Noise
• Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
• Regularization and Variable Selection Via the Elastic Net
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Most Tensor Problems Are NP-Hard
• Upper and Lower Bounds for Stochastic Processes
• Lower Bounds for Sparse Recovery
• Provable Sparse Tensor Decomposition

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