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

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.

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Cite this as

Yusuf Yigit Pilavci, Nicolas Farrugia (2025). Dataset: Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging. https://doi.org/10.57702/obe96336

DOI retrieved: January 3, 2025

Additional Info

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Created January 3, 2025
Last update January 3, 2025
Defined In https://doi.org/10.48550/arXiv.1910.05149
Author Yusuf Yigit Pilavci
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Nicolas Farrugia
Homepage https://neuroanatomyandconnectivity.github.io/opendata/