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Robust least squares estimation of graph signals

Citation Author(s):
Jari Miettinen, Sergiy Vorobyov, Esa Ollila
Submitted by:
Jari Miettinen
Last updated:
13 May 2019 - 12:55pm
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Document Year:
Presenters Name:
Jari Miettinen
Paper Code:



Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals. In simulation studies, we show that the randomized greedy robust least meansquares (RGRLMS) outperforms the regular LMS and has even more potential given a robust sampling design.

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