Robust Least Mean Squares Estimation of Graph Signals

Jari Miettinen; Sergiy Vorobyov; Esa Ollila

doi:10.1109/ICASSP.2019.8683193

Robust Least Mean Squares Estimation of Graph Signals

Jari Miettinen, Sergiy Vorobyov, Esa Ollila

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

2 Sitaatiot (Scopus)

136 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Kustantaja	IEEE
Sivut	5416-5420
Sivumäärä	5
ISBN (elektroninen)	978-1-4799-8131-1
ISBN (painettu)	978-1-4799-8132-8
DOI - pysyväislinkit	https://doi.org/10.1109/ICASSP.2019.8683193
Tila	Julkaistu - 1 toukok. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia Kesto: 12 toukok. 2019 → 17 toukok. 2019 Konferenssinumero: 44

Julkaisusarja

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

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Lyhennettä	ICASSP
Maa/Alue	Iso-Britannia
Kaupunki	Brighton
Ajanjakso	12/05/2019 → 17/05/2019

Pääsy asiakirjaan

10.1109/ICASSP.2019.8683193

ELEC_Miettinen_Robust_least_ICASSPAccepted author manuscript, 254 KB

Muut tiedostot ja linkit

Link to publication in Scopus

Robusteja tilastollisia menetelmiä hyvin moniulotteiselle datalle
Ollila, E. (Vastuullinen tutkija), Raninen, E. (Projektin jäsen), Basiri, S. (Projektin jäsen), Tabassum, M. N. (Projektin jäsen) & Mian, A. (Projektin jäsen)
01/09/2016 → 31/12/2020
Projekti: Academy of Finland: Other research funding
Lähettimen keila-avaruuden käyttö aktiivisessa kompressiivisessa aistimessa ja tiedonsiirrossa monen aaltomuodon
Upadhya, K. (Projektin jäsen), Gao, R. (Projektin jäsen), Vorobyov, S. (Vastuullinen tutkija), Li, Y. (Projektin jäsen), Rizwan Ullah, R. (Projektin jäsen), Ghorbani Veshki, F. (Projektin jäsen), Dosti, E. (Projektin jäsen) & Kocharlakota, K. (Projektin jäsen)
01/09/2016 → 31/08/2020
Projekti: Academy of Finland: Other research funding

Siteeraa tätä

Miettinen, J., Vorobyov, S., & Ollila, E. (2019). Robust Least Mean Squares Estimation of Graph Signals. teoksessa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings (Sivut 5416-5420). Artikkeli 8683193 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2019.8683193

Miettinen, Jari ; Vorobyov, Sergiy ; Ollila, Esa. / Robust Least Mean Squares Estimation of Graph Signals. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. Sivut 5416-5420 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

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abstract = "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.",

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Miettinen, J, Vorobyov, S & Ollila, E 2019, Robust Least Mean Squares Estimation of Graph Signals. julkaisussa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings., 8683193, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Sivut 5416-5420, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, Iso-Britannia, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8683193

Robust Least Mean Squares Estimation of Graph Signals. / Miettinen, Jari; Vorobyov, Sergiy ; Ollila, Esa.
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. s. 5416-5420 8683193 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Robust Least Mean Squares Estimation of Graph Signals

AU - Miettinen, Jari

AU - Vorobyov, Sergiy

AU - Ollila, Esa

N1 - Conference code: 44

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Graph signal processing

KW - Laplacian matrix

KW - least mean squares

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Miettinen J, Vorobyov S , Ollila E. Robust Least Mean Squares Estimation of Graph Signals. julkaisussa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE. 2019. s. 5416-5420. 8683193. (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). doi: 10.1109/ICASSP.2019.8683193

Robust Least Mean Squares Estimation of Graph Signals

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

Robusteja tilastollisia menetelmiä hyvin moniulotteiselle datalle

Lähettimen keila-avaruuden käyttö aktiivisessa kompressiivisessa aistimessa ja tiedonsiirrossa monen aaltomuodon

Siteeraa tätä