Minimum distance classification rules for high dimensional data

DOI10.1016/j.jmva.2005.09.014zbMath1112.62060OpenAlexW2094086078MaRDI QIDQ855920

Muni S. Srivastava

Publication date: 7 December 2006

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.09.014

zbMATH Keywords

misclassification errormultivariate normalsingular WishartFisher discriminant ruleMoore--Penrose inverse

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Theory of matrix inversion and generalized inverses (15A09)

Related Items (5)

Asymptotic properties of the misclassification rates for Euclidean distance discriminant rule in high-dimensional data ⋮ Estimation in High-Dimensional Analysis and Multivariate Linear Models ⋮ Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations ⋮ Change-Point Detection of the Mean Vector with Fewer Observations than the Dimension Using Instantaneous Normal Random Projections ⋮ The Asymptotic Approximation of EPMC for Linear Discriminant Rules Using a Moore-Penrose Inverse Matrix in High Dimension

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• An Asymptotic Expansion for the Distribution of the Linear Discriminant Function
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