23 citations · 64 across the 21 of their papers we have counts for
5 papers · 1 filter
-adaptive trees for graph signal approximation
Giacomo Elefante, Wolfgang Erb, Michael Multerer
Tree-encoded partitionings of graphs are fundamental tools for the decomposition and approximation of graph signals. For the efficient approximation of such graph signals, we devel…
Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets
Wolfgang Erb
We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph par…
Partition of Unity Methods for Signal Processing on Graphs
Roberto Cavoretto, Alessandra De Rossi, Wolfgang Erb
Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing. Based on a greedy-type metric clustering and augmentation sc…
Graph signal interpolation with Positive Definite Graph Basis Functions
Wolfgang Erb
For the interpolation of graph signals with generalized shifts of a graph basis function (GBF), we introduce the concept of positive definite functions on graphs. This concept merg…
Shapes of Uncertainty in Spectral Graph Theory
Wolfgang Erb
We present a flexible framework for uncertainty principles in spectral graph theory. In this framework, general filter functions modeling the spatial and spectral localization of a…