ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2014
Regularized matrix regression.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.Statistics in medicine · 2026Article
- Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.Diagnostics (Basel, Switzerland) · 2025Review
- Novel Scalar-on-matrix Regression for Unbalanced Feature Matrices.Statistics in biosciences · 2025Article
- Bayesian scalar-on-network regression with applications to brain functional connectivity.Biometrics · 2025Article
- Partial Quantile Tensor Regression.Journal of the American Statistical Association · 2024Article
- TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.The annals of applied statistics · 2024Article
- EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.Human brain mapping · 2024Article
- Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome.IEEE transactions on bio-medical engineering · 2024Article
- Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation.The American statistician · 2024Article
- A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2024Article
- Latent factor model for multivariate functional data.Biometrics · 2023Article
- Single-index models with functional connectivity network predictors.Biostatistics (Oxford, England) · 2022Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease.Journal of the American Statistical Association · 2022Article
- Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association.Journal of applied statistics · 2022Article
- TENSOR QUANTILE REGRESSION WITH APPLICATION TO ASSOCIATION BETWEEN NEUROIMAGES AND HUMAN INTELLIGENCE.The annals of applied statistics · 2021Article
- Article
- A Generalizable and Discriminative Learning Method for Deep EEG-Based Motor Imagery Classification.Frontiers in neuroscience · 2021Article
- L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses.Journal of the American Statistical Association · 2020Article
- A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.Biometrics · 2020Article
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2 authors.
Funding
Abstract
Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry and electroencephalography, matrix-type covariates frequently arise when measurements are obtained for each combination of two underlying variables. To address scientific questions arising from those data, new regression methods that take matrices as covariates are needed, and sparsity or other forms of regularization are crucial owing to the ultrahigh dimensionality and complex structure of the matrix data. The popular lasso and related regularization methods hinge on the sparsity of the true signal in terms of the number of its non-zero coefficients. However, for the matrix data, the true signal is often of, or can be well approximated by, a low rank structure. As such, the sparsity is frequently in the form of low rank of the matrix parameters, which may seriously violate the assumption of the classical lasso. We propose a class of regularized matrix regression methods based on spectral regularization. A highly efficient and scalable estimation algorithm is developed, and a degrees-of-freedom formula is derived to facilitate model selection along the regularization path. Superior performance of the method proposed is demonstrated on both synthetic and real examples.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.