# Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging

arXiv: https://arxiv.org/abs/1910.05149 · HTML: https://mlsanity.com/paper/1910.05149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification](https://mlsanity.com/paper/2108.10427/) (2021)
- [Comparing and Scaling fMRI Features for Brain-Behavior Prediction](https://mlsanity.com/paper/2507.20601/) (2025)

## Abstract (arXiv)

Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains. Exploiting transformations that are defined on graph models can be highly beneficial when the graph encodes relationships between signals. In this work, we present the benefits of using Spectral Graph Wavelet Transform (SGWT) as a feature extractor for machine learning on brain graphs. First, we consider a synthetic regression problem in which the smooth graph signals are generated as input with additive noise, and the target is derived from the input without noise. This enables us to optimize the spectrum coverage using different wavelet shapes. Finally, we present the benefits obtained by SGWT on a functional Magnetic Resonance Imaging (fMRI) open dataset on human subjects, with several graphs and wavelet shapes, by demonstrating significant performance improvements compared to the state of the art.