Local-aggregate modeling for big data via distributed optimization: Applications to neuroimaging

DOI10.1111/biom.12355zbMath1419.62372arXiv1405.0629OpenAlexW2119149751WikidataQ35752119 ScholiaQ35752119MaRDI QIDQ2809515

Yue Hu, Genevera I. Allen

Publication date: 30 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.0629

zbMATH Keywords

ADMMparallel computinggeneralized linear modelsbig dataensemble learningregularization pathsmulti-subject neuroimaging

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Biomedical imaging and signal processing (92C55)

Related Items (2)

Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data ⋮ Network classification with applications to brain connectomics

Uses Software

• glmnet

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• A two-way regularization method for MEG source reconstruction
• Generalized linear models
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• On the global and linear convergence of the generalized alternating direction method of multipliers
• The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions
• A Generalized Least-Square Matrix Decomposition
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Model Selection and Estimation in Regression with Grouped Variables
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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