Regularization through variable selection and conditional MLE with application to classification in high dimensions

DOI10.1016/j.jspi.2008.04.027zbMath1149.62052OpenAlexW2059118455MaRDI QIDQ958795

Eitan Greenshtein, Junyong Park, Guy Lebanon

Publication date: 8 December 2008

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2008.04.027

zbMATH Keywords

classificationhigh dimensionsconditional MLEStein's unbiased estimator

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)

Related Items

Estimating and testing conditional sums of means in high dimensional multivariate binary data ⋮ A guided random walk through some high dimensional problems ⋮ Two-group classification with high-dimensional correlated data: a factor model approach ⋮ SURE estimates for high dimensional classification ⋮ False Discovery Rate Based on Extreme Values in High Dimension

Uses Software

• EBayesThresh

Cites Work

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• 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
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• Empirical Bayes selection of wavelet thresholds
• Adapting to Unknown Smoothness via Wavelet Shrinkage
• Shrunken Estimators in Discriminant and Canonical Variate Analysis
• Ideal spatial adaptation by wavelet shrinkage