AI RESEARCH
SPPCSO: Adaptive Penalized Estimation Method for High-Dimensional Correlated Data
arXiv CS.LG
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ArXi:2603.06251v1 Announce Type: cross With the rise of high-dimensional correlated data, multicollinearity poses a significant challenge to model stability, often leading to unstable estimation and reduced predictive accuracy. This work proposes the Single-Parametric Principal Component Selection Operator (SPPCSO), an innovative penalized estimation method that integrates single-parametric principal component regression and $L_{1}$ regularization to adaptively adjust the shrinkage factor by incorporating principal component information.