Classification in High Dimension Using the Ledoit–Wolf Shrinkage Method

Classification using linear discriminant analysis (LDA) is challenging when the number of variables is large relative to the number of observations. Algorithms such as LDA require the computation of the feature vector’s precision matrices. In a high-dimension setting, due to the singularity of the c...

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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 10; no. 21; p. 4069
Main Authors:	Lotfi, Rasoul, Shahsavani, Davood, Arashi, Mohammad
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-11-2022
Subjects:	Algorithms Analysis Classification Covariance matrix Discriminant analysis Factor analysis Food science high-dimensional data Ledoit and Wolf shrinkage method linear discriminant analysis Matrices Maximum likelihood estimates Maximum likelihood estimates (Statistics) Maximum likelihood estimators Methods misclassification error Normal distribution Stein-type shrinkage Iran
Online Access:	Get full text
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