This is a manual for the software package GBFPUM, a MATLAB toolbox for the generation of a partition of unity on graphs and their application as an interpolation and approximation tool for graph signals. GBFPUM combines local kernel approximation based on graph basis functions (GBFs) with a partition of unity method (PUM) in order to obtain a low-cost global interpolation or classification scheme for large graphs. We give a detailed description of all the routines implemented in the MATLAB package and show how the code can be used to interpolate graph signals in concrete examples
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
The empirical interpolation method is an interpolation scheme with problem dependent basis functions...
Paper presented at the Conference on Algorithms for Approximation , Shrivenham (GB), 1988Available f...
The problem of constructing such a continuous function is called data fitting. Many times, data give...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
In this paper we propose a new stable and accurate approximation technique which is extremely effect...
Graph kernels have been studied for a long time and applied among others for graph classification. I...
International audienceAn intuitive and accessible text explaining the fundamentals and applications ...
http://deepblue.lib.umich.edu/bitstream/2027.42/5441/5/bac4069.0001.001.pdfhttp://deepblue.lib.umich...
This paper is dedicated to Prof. Francesco A. Costabile on the occasion of his 70th birthday In this...
This Matlab package is part of PhD thesis; title: Multilevel Quasi-Interpolation With Gaussian Kern...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
Interpolation based on radial basis functions (RBF) is a standard data map- ping method used in mul...
We present a general framework for extrapolation of kernels, which can be used to extend graph kerne...
We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sample...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
The empirical interpolation method is an interpolation scheme with problem dependent basis functions...
Paper presented at the Conference on Algorithms for Approximation , Shrivenham (GB), 1988Available f...
The problem of constructing such a continuous function is called data fitting. Many times, data give...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
In this paper we propose a new stable and accurate approximation technique which is extremely effect...
Graph kernels have been studied for a long time and applied among others for graph classification. I...
International audienceAn intuitive and accessible text explaining the fundamentals and applications ...
http://deepblue.lib.umich.edu/bitstream/2027.42/5441/5/bac4069.0001.001.pdfhttp://deepblue.lib.umich...
This paper is dedicated to Prof. Francesco A. Costabile on the occasion of his 70th birthday In this...
This Matlab package is part of PhD thesis; title: Multilevel Quasi-Interpolation With Gaussian Kern...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
Interpolation based on radial basis functions (RBF) is a standard data map- ping method used in mul...
We present a general framework for extrapolation of kernels, which can be used to extend graph kerne...
We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sample...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
The empirical interpolation method is an interpolation scheme with problem dependent basis functions...
Paper presented at the Conference on Algorithms for Approximation , Shrivenham (GB), 1988Available f...