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eess.SP2026
Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation
Saghar Bagheri, Gene Cheung, Tim Eadie +1
A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed…
eess.SP2020
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Saghar Bagheri, Gene Cheung, Antonio Ortega +1
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising. Previous graph learnin…