6 papers
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…
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…
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…
Unrolling Graph-based Douglas-Rachford Algorithm for Image Interpolation with Informed Initialization
Xue Zhang, Bingshuo Hu, Gene Cheung
Conventional deep neural nets (DNNs) initialize network parameters at random and then optimize each one via stochastic gradient descent (SGD), resulting in substantial risk of poor…
Unrolling Nonconvex Graph Total Variation for Image Denoising
Songlin Wei, Gene Cheung, Fei Chen +1
Conventional model-based image denoising optimizations employ convex regularization terms, such as total variation (TV) that convexifies the -norm to promote sparse signal…
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration
Jianghe Cai, Gene Cheung, Fei Chen
Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large para…