Work

LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models

Year: 2025

Type: article

Abstract: approach that further mitigates overfitting in high-dimensional settings beyond standard regularizers such as the lasso and elastic net. We demonstrate that our approach, called LIT-LVM, achieves superior prediction accuracy compared to the elastic net, hierarchical lasso, and factorization machines on a wide variety of simulated and real data, particularly when the number of interaction terms is high compared to the number of samples. LIT-LVM also provides low-dimensional latent representations for features that are useful for visualizing and analyzing their relationships.

Source: PubMed

DOI: 10.48550/arxiv.2506.15492

Authors: Nemati, Mohammadreza, Huang, Zhipeng, Xu, Kevin S.

Institutions: Case Western Reserve University, Suffolk University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Hennepin Healthcare Research Institute, National Institutes of Health, U.S. National Library of Medicine

Awards: Machine Learning and Network Science for Predicting Kidney Transplant Survival , CAREER: Model-based Analysis of Dynamic Networks using Continuous-time Network Models