Modified linear discriminant analysis using block covariance matrix in high-dimensional data

DOI10.1080/03610918.2015.1014103zbMath1422.62242OpenAlexW2547768689MaRDI QIDQ5267876

Jin Hyun Nam, Donguk Kim

Publication date: 13 June 2017

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2015.1014103

zbMATH Keywords

classificationhigh-dimensionblock covariance matrixmodified linear discriminant analysis (LDA)

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Measures of association (correlation, canonical correlation, etc.) (62H20)

Cites Work

• Regularized linear discriminant analysis and its application in microarrays
• An extensive comparison of recent classification tools applied to microarray data
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Comparison of Support Vector Machines to Other Classifiers Using Gene Expression Data
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data
• Gene selection for cancer classification using support vector machines

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