General sparse multi-class linear discriminant analysis

DOI10.1016/J.CSDA.2016.01.011zbMath1468.62170OpenAlexW2271208534MaRDI QIDQ1659183

Sandra E. Safo, Jeongyoun Ahn

Publication date: 15 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2016.01.011

zbMATH Keywords

classificationsingular value decompositionlinear discriminant analysismulti-class discriminationsparse discrimination

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (7)

Unobserved classes and extra variables in high-dimensional discriminant analysis ⋮ Network-based discriminant analysis for multiclassification ⋮ Variational discriminant analysis with variable selection ⋮ Sparse linear discriminant analysis in structured covariates space ⋮ Multiclass analysis and prediction with network structured covariates ⋮ NetDA: an R package for network-based discriminant analysis subject to multilabel classes ⋮ Hierarchical clustered multiclass discriminant analysis via cross-validation

Uses Software

• CVX
• rda
• penalizedLDA
• recount
• boost

Cites Work

• Unnamed Item
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• Sparse linear discriminant analysis by thresholding for high dimensional data
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Graph Implementations for Nonsmooth Convex Programs
• Penalized Classification using Fisher’s Linear Discriminant
• A direct approach to sparse discriminant analysis in ultra-high dimensions
• A Direct Estimation Approach to Sparse Linear Discriminant Analysis
• Flexible Discriminant Analysis by Optimal Scoring
• Sparsity and Smoothness Via the Fused Lasso
• The maximal data piling direction for discrimination
• Multicategory Support Vector Machines

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