Feature selection guided by structural information

DOI10.1214/09-AOAS302zbMath1194.62092arXiv1011.2315MaRDI QIDQ993274

Martin Slawski, Wolfgang Zu Castell, Gerhard Tutz

Publication date: 10 September 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1011.2315

zbMATH Keywords

regularizationmodel selectionrandom fieldssparsitygeneralized linear modellassoelastic net\(p \gg n\)signal regression

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Generalized linear models (logistic models) (62J12)

Related Items

Structured regularization for conditional Gaussian graphical models, On iteration complexity of a first-order primal-dual method for nonlinear convex cone programming, Unnamed Item, Studies of the adaptive network-constrained linear regression and its application, Noise-robust semi-supervised learning via fast sparse coding, Feature-Weighted Elastic Net: Using "Features of Features" for Better Prediction, Connectivity‐informed adaptive regularization for generalized outcomes, Network-based sparse Bayesian classification, Structured penalties for functional linear models -- partially empirical eigenvectors for regression, A significance test for graph‐constrained estimation, Feature selection guided by structural information, The structured elastic net for quantile regression and support vector classification, Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals, Adaptive elastic-net selection in a quantile model with diverging number of variable groups, A partial graphical model with a structural prior on the direct links between predictors and responses, The Adaptive Gril Estimator with a Diverging Number of Parameters

Uses Software

• rda
• GMRFLib

Cites Work

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