Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

DOI10.1016/j.csda.2013.04.003zbMath1471.62206arXiv1206.1660OpenAlexW1999762767MaRDI QIDQ1800123

Cheng Wang, Baiqi Miao, Longbing Cao

Publication date: 19 October 2018

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

Full work available at URL: https://arxiv.org/abs/1206.1660

zbMATH Keywords

naive Bayesmisclassification ratefeature selectionlarge \(p\), small \(n\)linear discriminant analysis (LDA)high-dimensional classification

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 (3)

High dimensional discrimination analysis via a semiparametric model ⋮ Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon ⋮ Variable selection of linear programming discriminant estimator

Uses Software

• sma

Cites Work

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• Stable recovery of sparse overcomplete representations in the presence of noise
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Comparison of Discrimination Methods for High Dimensional Data
• A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant

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