An adapted linear discriminant analysis with variable selection for the classification in high-dimension, and an application to medical data

DOI10.1016/j.csda.2020.107031OpenAlexW3036493023MaRDI QIDQ830539

Khuyen T. Le, Frédéric J. P. Richard, Caroline Chaux, Eric Guedj

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2020.107031

zbMATH Keywords

classificationvariable selectionAlzheimer's diseaselinear discriminant analysisgraphical Lassoprecision matrix estimationPET imaging

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

Unobserved classes and extra variables in high-dimensional discriminant analysis ⋮ Multiclass sparse discriminant analysis incorporating graphical structure among predictors ⋮ Neyman's truncation test for two-sample means under high dimensional setting

Uses Software

• glasso
• UNLocBoX
• scout
• msda

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Sparse inverse covariance estimation with the graphical lasso
• The graphical lasso: new insights and alternatives
• An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss
• Proximal Splitting Methods in Signal Processing
• Covariance-Regularized Regression and Classification for high Dimensional Problems
• A direct approach to sparse discriminant analysis in ultra-high dimensions
• A Direct Estimation Approach to Sparse Linear Discriminant Analysis
• Model selection and estimation in the Gaussian graphical model
• Multiclass Sparse Discriminant Analysis
• A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant

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