---
license: bsd-3-clause
tags:
- binary-classification
- graph-fourier-transform
- likelihood-ratio-test
- healthcare
---


# Graph-structured data classification based on spectral methods and generalized likelihood-ratio testing

This page documents the reference implementation used in the following work on binary classification of graph-structured data applied to computer-aided diagnosis in neurology.

## Blog article 
 - **Title:** Graph-structured data classification based on spectral methods and the generalized likelihood ratio test
 - **Author:** Michel Pohl
 - **Year:** 2026
 - **Link:** https://pohl-michel.github.io/blog/articles/fourier-glrt-graph-classification/article.html

## Code
 - **GitHub repository:** https://github.com/pohl-michel/fourier-glrt-based-graph-classification

## Description
I implemented a binary classifier for graph-structured data, combining generalized likelihood ratio testing and graph Fourier transforms, based on [prior work from Hu et al. (2016)](https://doi.org/10.1016/j.neuroimage.2015.10.026). I applied this method to Alzheimer's disease detection by modeling brain regions in PET images as nodes in a graph. The edge weights, representing similarity between regional imaging feature values, were computed using a Gaussian RBF kernel. This approach yielded a leave-one-out test F1 score of 0.85 on a dataset of 142 brain scans (61 healthy controls and 81 Alzheimer's disease cases).

## Note

The blog article is based on work conducted jointly at Centrale Méditerranée and Fresnel Institute in 2016 under the supervision of Mouloud Adel.