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Partition of Unity Methods for Signal Processing on Graphs
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 scheme, we show how a partition of unity can be generated in an efficient way on graphs. We investigate how PUMs can be co...
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Published in: | The Journal of fourier analysis and applications 2021-08, Vol.27 (4), Article 66 |
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container_title | The Journal of fourier analysis and applications |
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creator | Cavoretto, Roberto De Rossi, Alessandra Erb, Wolfgang |
description | 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 scheme, we show how a partition of unity can be generated in an efficient way on graphs. We investigate how PUMs can be combined with a local graph basis function (GBF) approximation method in order to obtain low-cost global interpolation or classification schemes. From a theoretical point of view, we study necessary prerequisites for the partition of unity such that global error estimates of the PUM follow from corresponding local ones. Finally, properties of the PUM as cost-efficiency and approximation accuracy are investigated numerically. |
doi_str_mv | 10.1007/s00041-021-09871-w |
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subjects | Abstract Harmonic Analysis Approximation Approximations and Expansions Basis functions Clustering Fourier Analysis Graphs Harmonic Analysis on Combinatorial Graphs Interpolation Mathematical analysis Mathematical Methods in Physics Mathematics Mathematics and Statistics Methods Partial Differential Equations Signal processing Signal,Image and Speech Processing Unity |
title | Partition of Unity Methods for Signal Processing on Graphs |
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