Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging
This work addresses feature extraction in neuroimaging for researchers, but it is incremental as it applies an existing transformation to a new domain.
The authors tackled the problem of extracting features from brain graphs for machine learning by using Spectral Graph Wavelet Transform (SGWT), resulting in significant performance improvements on an fMRI dataset compared to state-of-the-art methods.
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.