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Smooth graph learning for functional connectivity estimation

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0 Published in 2021 at "NeuroImage"

DOI: 10.1016/j.neuroimage.2021.118289

Abstract: Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) signals is important in understanding neural representation and information processing in cortical networks. However, due to a lack of "ground truth" FC pattern, the reliability… read more here.

Keywords: graph learning; graph; smooth graph; sgfc ... See more keywords

Joint Graph Learning and Blind Separation of Smooth Graph Signals Using Minimization of Mutual Information and Laplacian Quadratic Forms

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2 Published in 2023 at "IEEE Transactions on Signal and Information Processing over Networks"

DOI: 10.1109/tsipn.2023.3240893

Abstract: The smoothness of graph signals has found desirable real applications for processing irregular (graph-based) signals. When the latent sources of the mixtures provided to us as observations are smooth graph signals, it is more efficient… read more here.

Keywords: information; graph signals; smooth graph; graph signal ... See more keywords

Online Smooth Graph Learning From Incomplete Data

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0 Published in 2025 at "IEEE Transactions on Signal and Information Processing over Networks"

DOI: 10.1109/tsipn.2025.3589719

Abstract: Graphs are essential for extracting crucial information embedded within structured data and are foundational tools across various fields. Predefined graphs, however, cannot adequately capture the intrinsic relationships within data, highlighting the need for learning graphs… read more here.

Keywords: smooth graph; incomplete data; graph learning; online smooth ... See more keywords