Maximizing adjusted covariance: new supervised dimension reduction for classification

Hyejoon Park (), Hyunjoong Kim () and Yung-Seop Lee ()
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Hyejoon Park: Yonsei University
Hyunjoong Kim: Yonsei University
Yung-Seop Lee: Dongguk University

Computational Statistics, 2025, vol. 40, issue 1, No 22, 573-599

Abstract: Abstract This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised classification. The new approach is to adjust the covariance matrix between input and target variables using the within-class sum of squares, thereby promoting class separation after linear dimension reduction. MAC has a low computational cost and can complement existing linear dimensionality reduction techniques for classification. In this study, the classification performance by MAC was compared with those of the existing linear dimension reduction methods using 44 datasets. In most of the classification models used in the experiment, the MAC dimension reduction method showed better classification accuracy and F1 score than other linear dimension reduction methods.

Keywords: Linear dimension reduction; Classification; Principal component analysis; Canonical linear discriminant analysis; Partial least squares - discriminant analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01472-7

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