10 papers
Graph Signal Surrogate Generation for Statistical Testing of Covariance Structure on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Non-parametric statistical testing is based on surrogate data generation that randomizes chosen features in the empirical data. In the graph setting, graph signal processing (GSP)…
Graph Diffusion-Advection Operator for Directed Graph Signal Processing
CHM. Chan, A. Cionca, V. Škultéty +1
Graph signal processing (GSP) provides a framework for analyzing data on irregular domains, with applications in neuroscience, finance, chemistry, and social sciences. Classical GS…
Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Graph signal processing has opened new avenues to the canonical denoising problem in interesting settings. Specifically, here we propose a Wiener-filter solution for graph signals…
Statistical Testing on Directed Graphs by Surrogate Data Generation
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals…
Topological Signal Processing: An Application-Oriented Tutorial
Flavia Petruso, Maria Giulia Preti, Dimitri Van De Ville
Many modern datasets are large and carry complex structural relationships. Graph-based methods have traditionally been used to represent networked data, modeling individual element…
Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
Enamundram Naga Karthik, Sandrine Bédard, Jan Valošek +53
Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. Whil…