A method for selecting the relevant dimensions for high-dimensional classification in singular vector spaces

DOI10.1007/S11634-018-0311-8zbMath1459.62123OpenAlexW2785046661MaRDI QIDQ1999449

Mark H. Carpenter, Dawit G. Tadesse

Publication date: 27 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-018-0311-8

zbMATH Keywords

text miningnaive Bayesfeature selectionsingular value decomposition (SVD)Fisher discriminant

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

• High-dimensional classification using features annealed independence rules
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• A direct approach to sparse discriminant analysis in ultra-high dimensions
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Sure Independence Screening for Ultrahigh Dimensional Feature Space

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