Robust Least Mean Squares Estimation of Graph Signals

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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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 mean squares (RGRLMS) outperforms the regular LMS and has even more potential given a robust sampling design.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
TilaJulkaistu - 1 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia
Kesto: 12 toukokuuta 201917 toukokuuta 2019
Konferenssinumero: 44

Julkaisusarja

NimiProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (painettu)1520-6149
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
MaaIso-Britannia
KaupunkiBrighton
Ajanjakso12/05/201917/05/2019

ID: 33983232