6 papers
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…
Fast Graph Sampling Set Selection Using Gershgorin Disc Alignment
Yuanchao Bai, Fen Wang, Gene Cheung +2
Graph sampling set selection, where a subset of nodes are chosen to collect samples to reconstruct a smooth graph signal, is a fundamental problem in graph signal processing (GSP).…
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
Fen Wang, Yongchao Wang, Gene Cheung +1
Matrix completion algorithms fill missing entries in a large matrix given a subset of observed samples. However, how to best pre-select informative matrix entries given a sampling…
Low-complexity Graph Sampling with Noise and Signal Reconstruction via Neumann Series
Fen Wang, Gene Cheung, Yongchao Wang
Graph sampling addresses the problem of selecting a node subset in a graph to collect samples, so that a K-bandlimited signal can be reconstructed in high fidelity. Assuming an ind…
Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment
Yuanchao Bai, Gene Cheung, Fen Wang +2
Graph sampling with noise is a fundamental problem in graph signal processing (GSP). Previous works assume an unbiased least square (LS) signal reconstruction scheme and select sam…
A-Optimal Sampling and Robust Reconstruction for Graph Signals via Truncated Neumann Series
Fen Wang, Yongchao Wang, Gene Cheung
Graph signal processing (GSP) studies signals that live on irregular data kernels described by graphs. One fundamental problem in GSP is sampling---from which subset of graph nodes…