SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer's Disease Classification.

Journal: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Published Date: Jun 10, 2025

Abstract

Logistic regression is a widely used model in machine learning, particularly as a baseline for binary classification tasks due to its simplicity, effectiveness, and interpretability. It is especially powerful when dealing with categorical features. Despite its advantages, standard logistic regression fails to capture the distributional and geometric structure of data, especially when features are derived from structured spaces like brain imaging. For instance, in Voxel-Based Morphometry (VBM), measurements from distinct brain regions follow a clear spatial organization, which standard logistic regression cannot fully leverage. In this paper, we propose Sinkhorn Logistic Regression (SLR), a variant of logistic regression that incorporates the Sinkhorn divergence as a loss function. This adaptation enables the model to leverage geometric information about the data distribution, enhancing its performance on structured datasets.

Authors

  • Qipeng Zhan
    University of Pennsylvania, Philadelphia, PA, USA.
  • Zhuoping Zhou
    University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Zixuan Wen
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Zexuan Wang
    School of Psychology, Beijing Sport University, No. 48 Xinxi Road Haidian Distric, Beijing, 100084, China.
  • Boning Tong
    University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Heng Huang
    Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA.
  • Andrew J Saykin
    Indiana University, Indianapolis, IN 46202, USA.
  • Paul M Thompson
    Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

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