1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Saghar Bagheri, Gene Cheung, Tim Eadie
In agronomics, predicting crop yield at a per field/county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-of-the…
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Saghar Bagheri, Chinthaka Dinesh, Gene Cheung +1
Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield predict…
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Chinthaka Dinesh, Gene Cheung, Saghar Bagheri +1
A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. Howe…
Hybrid Model-based / Data-driven Graph Transform for Image Coding
Saghar Bagheri, Tam Thuc Do, Gene Cheung +1
Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Loève transform (KLT) computed from an empirical covariance…
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…