Work

Error Function Regularization: A New Approach to Sparse Logistic Regression

Year: 2026

Type: other

Abstract: We introduce the ERror Function (ERF) for sparse logistic regression. The ERF regularization interpolates between the L0 norm, which measures the number of non-zero elements in a vector, and its convex relaxation, the L1 norm, controlled by a hyper-parameter. Building on an Iterative Reweighted L1 (IRL1) scheme and the Alternating Direction Method of Multipliers (ADMM), we develop an effective algorithm to minimize the proposed model and analyze its convergence. Experiments on both synthetic and real high-dimensional datasets demonstrate the empirical competitiveness of our approach.

Source: Carolina Digital Repository (University of North Carolina at Chapel Hill)

DOI: 10.17615/474f-zb11

Author: John Albright

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7123475410

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