TY - JOUR
T1 - Sparse principal component regression via singular value decomposition approach
AU - Kawano, Shuichi
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/9
Y1 - 2021/9
N2 - Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.
AB - Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.
KW - ADMM
KW - Lasso
KW - One-stage procedure
KW - Principal component analysis
KW - Singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=85100713204&partnerID=8YFLogxK
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U2 - 10.1007/s11634-020-00435-2
DO - 10.1007/s11634-020-00435-2
M3 - Article
AN - SCOPUS:85100713204
SN - 1862-5347
VL - 15
SP - 795
EP - 823
JO - Advances in Data Analysis and Classification
JF - Advances in Data Analysis and Classification
IS - 3
ER -