A Fast Screening Approach for High-dimensional Outcomes and High-dimensional Predictors
Modeling interactions among multimodal, high-dimensional data is intrinsically challenging due to ultra-high dimensionality and complex dependence structure with high level noise. Screening methods are effective for reducing dimensionality, but most existing approaches shrink only the predictor space while retaining all outcomes. In cross-modal analyses, different outcomes often select different predictor subsets, so the union remains large and the response dimension is unchanged, limiting the practical benefit of screening. This gives rise to heavy computational burdens and poor interpretability. To address these limitations, we propose a new screening framework, Graph Independence Dual ...
| Authors | Hongju Park, Zhenyao Ye, Shuo Chen |
|---|---|
| Primary category | stat.ME |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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