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New adaptive subsampling method enhances feature screening efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new adaptive subsampling method for feature screening in ultrahigh-dimensional datasets, aiming to improve computational efficiency over the standard Sure Independence Screening (SIS) method. This new approach, inspired by multi-armed bandit problems, reduces the computational cost from O(np) to O(sqrt(n)p) by progressively increasing subsample size and eliminating unpromising features. The method retains the sure screening property and demonstrates comparable screening and prediction performance to SIS in experiments, while significantly reducing computation time. AI

IMPACT Offers a more computationally efficient approach for feature screening in large-scale, high-dimensional datasets, potentially benefiting AI model development.

RANK_REASON Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New adaptive subsampling method enhances feature screening efficiency

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Academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xiaxue Ouyang, Kejun He, Cheng Meng ·

    An adaptive subsampling method for large-sample feature screening

    arXiv:2509.16085v2 Announce Type: replace Abstract: We consider the sure independence screening (SIS) method, a standard feature screening approach that aims to eliminate non-informative features in ultrahigh-dimensional datasets. Although effective, SIS incurs a computational co…

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