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

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

145 Downloads (Pure)

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.

Original language	English
Title of host publication	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Publisher	IEEE
Pages	5416-5420
Number of pages	5
ISBN (Electronic)	978-1-4799-8131-1
ISBN (Print)	978-1-4799-8132-8
DOIs	https://doi.org/10.1109/ICASSP.2019.8683193
Publication status	Published - 1 May 2019
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44

Publication series

Name	Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP
Country/Territory	United Kingdom
City	Brighton
Period	12/05/2019 → 17/05/2019

Keywords

Graph signal processing
Laplacian matrix
least mean squares

Access to Document

10.1109/ICASSP.2019.8683193

ELEC_Miettinen_Robust_least_ICASSPAccepted author manuscript, 254 KB

Cite this

Miettinen, J., Vorobyov, S., & Ollila, E. (2019). Robust Least Mean Squares Estimation of Graph Signals. In 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings (pp. 5416-5420). Article 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. pp. 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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author = "Jari Miettinen and Sergiy Vorobyov and Esa Ollila",

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Miettinen, J, Vorobyov, S & Ollila, E 2019, Robust Least Mean Squares Estimation of Graph Signals. in 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, pp. 5416-5420, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 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. p. 5416-5420 8683193 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Robust Least Mean Squares Estimation of Graph Signals

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AU - Vorobyov, Sergiy

AU - Ollila, Esa

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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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SN - 978-1-4799-8132-8

T3 - Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

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BT - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings

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Miettinen J, Vorobyov S , Ollila E. Robust Least Mean Squares Estimation of Graph Signals. In 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE. 2019. p. 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

Abstract

Publication series

Conference

Keywords

Access to Document

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Transmit beamspace for active compressive sensing and communication with multiple waveforms

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Cite this

Robust Least Mean Squares Estimation of Graph Signals

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Transmit beamspace for active compressive sensing and communication with multiple waveforms

Robust Statistics for High-dimensional Data

Cite this