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README.md

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 license: bsd-3-clause
+tags:
+- binary-classification
+- graph-fourier-transform
+- likelihood-ratio-test
+- healthcare
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+
+
+# 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 using the graph Fourier transform and generalized likelihood-ratio test (GLRT).
+
+## 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.