5 accepted papers
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems…
In this paper, we propose a method, based on graph signal processing, to optimize the choice of k in k-nearest neighbor graphs (kNNGs). kNN is one of the most popular approaches and is widely used in machine learning and signal processing. The parameter k represents the number of neighbors that are…
We propose a sampling set selection method for graph signals under arbitrary signal priors. Most approaches of graph signal sampling assume that signals are bandlimited. However, in practical situations, there exist many full-band graph signals like piecewise smooth/constant signals. Our sampling se…
We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph s…
We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and assuming an appropriate prior. In this paper, w…