# RaSE: A Variable Screening Framework via Random Subspace Ensembles

arXiv: https://arxiv.org/abs/2102.03892 · HTML: https://mlsanity.com/paper/2102.03892/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Random-projection ensemble classification](https://mlsanity.com/paper/1504.04595/) (2015)
- [Tuning parameter selection in high dimensional penalized likelihood](https://mlsanity.com/paper/1605.03321/) (2016)
- [High-dimensional Ordinary Least-squares Projection for Screening Variables](https://mlsanity.com/paper/1506.01782/) (2015)
- [The fused Kolmogorov filter: A nonparametric model-free screening method](https://mlsanity.com/paper/1403.7701/) (2014)
- [RaSE: Random Subspace Ensemble Classification](https://mlsanity.com/paper/2006.08855/) (2020)
- [Variable selection for general index models via sliced inverse regression](https://mlsanity.com/paper/1304.4056/) (2013)
- [Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data](https://mlsanity.com/paper/1308.3942/) (2013)
- [Interaction Pursuit with Feature Screening and Selection](https://mlsanity.com/paper/1605.08933/) (2016)
- [Regularization after retention in ultrahigh dimensional linear regression models](https://mlsanity.com/paper/1311.5625/) (2013)
- [Interaction pursuit in high-dimensional multi-response regression via distance correlation](https://mlsanity.com/paper/1605.03315/) (2016)
- [Nonparametric Screening under Conditional Strictly Convex Loss for Ultrahigh Dimensional Sparse Data](https://mlsanity.com/paper/1912.01157/) (2019)

## Built on by (most cited first, 2 of 2)

- [Linear Discriminant Analysis with High-dimensional Mixed Variables](https://mlsanity.com/paper/2112.07145/) (2021)
- [Two-stage Hypothesis Tests for Variable Interactions with FDR Control](https://mlsanity.com/paper/2209.00077/) (2022)

## Abstract (arXiv)

Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors according to their individual contributions to the response. As a result, variables that are marginally independent but jointly dependent with the response could be missed. In this work, we propose a new framework for variable screening, Random Subspace Ensemble (RaSE), which works by evaluating the quality of random subspaces that may cover multiple predictors. This new screening framework can be naturally combined with any subspace evaluation criterion, which leads to an array of screening methods. The framework is capable to identify signals with no marginal effect or with high-order interaction effects. It is shown to enjoy the sure screening property and rank consistency. We also develop an iterative version of RaSE screening with theoretical support. Extensive simulation studies and real-data analysis show the effectiveness of the new screening framework.