Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915732443365376 |
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| author | Yu, Yue Wang, Guanghui Liu, Liu Zou, Changliang |
| author_facet | Yu, Yue Wang, Guanghui Liu, Liu Zou, Changliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10357 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning Yu, Yue Wang, Guanghui Liu, Liu Zou, Changliang Methodology Statistics Theory 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. |
| title | Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2601.10357 |