Envelope dimension reduction with application to binary classification

SOALE Abdul-Nasah, DONG Yuexiao

Journal of Systems Science & Complexity ›› 2025

PDF(339 KB)
PDF(339 KB)
Journal of Systems Science & Complexity ›› 2025

Envelope dimension reduction with application to binary classification

  • SOALE Abdul-Nasah1, DONG Yuexiao2
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Abstract

Classical linear discriminant analysis (LDA) (Fisher, 1936) implicitly assumes the classification boundary depends on only one linear combination of the predictors. This restriction can lead to poor classification in applications where the decision boundary depends on multiple linear combinations of the predictors. To overcome this challenge, we first project the predictors onto an envelope central space and then perform LDA based on the sufficient predictor. The performance of the proposed method in improving classification accuracy is demonstrated in both synthetic data and real applications.

Key words

Envelope linear regression / Linear discriminant analysis / Sliced inverse regression / Sufficient dimension reduction

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SOALE Abdul-Nasah , DONG Yuexiao. Envelope dimension reduction with application to binary classification. Journal of Systems Science & Complexity, 2025
PDF(339 KB)

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