TY - JOUR
T1 - Maximizing adjusted covariance
T2 - new supervised dimension reduction for classification
AU - Park, Hyejoon
AU - Kim, Hyunjoong
AU - Lee, Yung Seop
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Canonical linear discriminant analysis
KW - Classification
KW - Linear dimension reduction
KW - Partial least squares - discriminant analysis
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85189347394&partnerID=8YFLogxK
U2 - 10.1007/s00180-024-01472-7
DO - 10.1007/s00180-024-01472-7
M3 - Article
AN - SCOPUS:85189347394
SN - 0943-4062
JO - Computational Statistics
JF - Computational Statistics
ER -