Covariance structure approximation via gLasso in high-dimensional supervised classification

DOI10.1080/02664763.2012.663346zbMath1473.62227OpenAlexW2089723781MaRDI QIDQ3168288

Tatjana Pavlenko, Annika Tillander, Anders Björkström

Publication date: 30 October 2012

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2012.663346

zbMATH Keywords

high dimensionalitysparsitygraphical Lassoclassification accuracyblock-diagonal covariance structureseparation strength

Mathematics Subject Classification ID

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

Related Items (8)

Testing block‐diagonal covariance structure for high‐dimensional data ⋮ Block-diagonal test for high-dimensional covariance matrices ⋮ Supervised classifiers for high-dimensional higher-order data with locally doubly exchangeable covariance structure ⋮ Testing block-diagonal covariance structure for high-dimensional data under non-normality ⋮ Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models ⋮ Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models ⋮ Bayesian Block-Diagonal Predictive Classifier for Gaussian Data ⋮ Goodness-of-Fit Tests Based on Sup-Functionals of Weighted Empirical Processes

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• Moments of linear discriminant functions and an asymptotic confidence interval for the log odds ratio
• The Burbea-Rao and Bhattacharyya Centroids
• Comparison of Discrimination Methods for High Dimensional Data

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