no code implementations • 29 Oct 2023 • Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo
Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations.
no code implementations • 9 Dec 2022 • Aref Einizade, Samaneh Nasiri, Sepideh Hajipour Sardouie, Gari Clifford
The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology.
no code implementations • 24 Nov 2022 • Aref Einizade, Sepideh Hajipour Sardouie
Numerous approaches have been proposed to discover causal dependencies in machine learning and data mining; among them, the state-of-the-art VAR-LiNGAM (short for Vector Auto-Regressive Linear Non-Gaussian Acyclic Model) is a desirable approach to reveal both the instantaneous and time-lagged relationships.
no code implementations • 5 Nov 2022 • Aref Einizade, Sepideh Hajipour Sardouie
To address this issue and in the case of the underlying graph having graph product structure, we propose learning product (high dimensional) graphs from product spectral templates with significantly reduced complexity rather than learning them directly from high-dimensional graph signals, which, to the best of our knowledge, has not been addressed in the related areas.