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Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Specifically, we first show that greedily removing an edge at a time that induces the minimal change in the second eigenvalue leads to a sparse graph with good GCN performance.
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Prediction of annual crop yields at a county granularity is important for national food production and price stability.
Hybrid Model-based / Data-driven Graph Transform for Image Coding
Transform coding to sparsify signal representations remains crucial in an image compression pipeline.
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising.
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