Classification in High Dimension Using the Ledoit–Wolf Shrinkage Method

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
Main Authors: Rasoul Lotfi, Davood Shahsavani, Mohammad Arashi
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Mathematics
Subjects:
classification
linear discriminant analysis
high-dimensional data
Ledoit and Wolf shrinkage method
Stein-type shrinkage
misclassification error
Online Access:https://www.mdpi.com/2227-7390/10/21/4069

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https://www.mdpi.com/2227-7390/10/21/4069

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