ArticlePloS one2020
Logistic regression with image covariates via the combination of L1 and Sobolev regularizations.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative Analysis of Logistic Regression and Machine Learning Algorithms for Predicting De Novo Stress Urinary Incontinence or Symptomatic Worsening Following Pelvic Floor Reconstruction: A Multicenter Study.International journal of women's health · 2026Article
- A novel interpretable machine learning framework for predicting postpartum depression: a SHAP-based analysis of maternal and infant health indicators.Frontiers in psychiatry · 2026Article
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Authors and funding
2 authors.
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Abstract
The use of image covariates to build a classification model has lots of impact in various fields, such as computer science, medicine, and so on. The aim of this paper is to develop an estimation method for logistic regression model with image covariates. We propose a novel regularized estimation approach, where the regularization is a combination of L1 regularization and Sobolev norm regularization. The L1 penalty can perform variable selection, while the Sobolev norm penalty can capture the shape edges information of image data. We develop an efficient algorithm for the optimization problem. We also establish a nonasymptotic error bound on parameter estimation. Simulated studies and a real data application demonstrate that our proposed method performs very well.
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