Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning

Yue Yu; Guanghui Wang; Liu Liu; Changliang Zou

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arXiv · 2601.10357

Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning

Yue Yu·Guanghui Wang·Liu Liu·Changliang Zou

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Abstract

Dimension reduction is a fundamental tool for analyzing high-dimensional data in supervised learning. Traditional methods for estimating intrinsic order often prioritize model-specific structural assumptions over predictive utility. This paper introduces predictive order determination (POD), a model-agnostic framework that determines the minimal predictively sufficient dimension by directly evaluating out-of-sample predictiveness. POD quantifies uncertainty via error bounds for over- and underestimation and achieves consistency under mild conditions. By unifying dimension reduction with predictive performance, POD applies flexibly across diverse reduction tasks and supervised learners. Simulations and real-data analyses show that POD delivers accurate, uncertainty-aware order estimates, making it a versatile component for prediction-centric pipelines.

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Yue Yu, Guanghui Wang, Liu Liu, Changliang Zou. 2026-01-15. Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning. https://arxiv.org/abs/2601.10357

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