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cs.LG2026
Graph Learning with Spectral Connectivity Priors for Scarce Data
Mingxiao Liu, Bahar Oveisgharan, Bingyan Zou +3
Learning a sparse graph from scarce data is practically important but challenging. Motivated by the desirable combination of local sparsity and strong global connectivity exhibited…
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.SP2026
Sparse Graph Learning from Sparse Data via Fiedler Number Maximization
Bahar Oveisgharan, Gene Cheung, Andrew Eckford
We aim to learn a sparse and connected graph from sparse data, where the number of observations K can be substantially smaller than the signal dimension N for signals x in R^N, and…