Safe Screening for Logistic Regression with ℓ –ℓ Regularization

Anna Deza, Alper Atamtürk

2022

Abstract

In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with ℓ0 − ℓ2 regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations.

Paper Citation

in Harvard Style

Deza A. and Atamtürk A. (2022). Safe Screening for Logistic Regression with ℓ –ℓ Regularization. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 119-126. DOI: 10.5220/0011578100003335

in Bibtex Style

@conference{kdir22,
author={Anna Deza and Alper Atamtürk},
title={Safe Screening for Logistic Regression with ℓ –ℓ Regularization},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={119-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011578100003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Safe Screening for Logistic Regression with ℓ –ℓ Regularization
SN - 978-989-758-614-9
AU - Deza A.
AU - Atamtürk A.
PY - 2022
SP - 119
EP - 126
DO - 10.5220/0011578100003335
PB - SciTePress