Robust Least Mean Squares Estimation of Graph Signals

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A4 Artikkeli konferenssijulkaisussa
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Date

2019-05-01

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Department of Signal Processing and Acoustics

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Mcode

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Language

en

Pages

5
5416-5420

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ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

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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Keywords

Graph signal processing, Laplacian matrix, least mean squares

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DOI

10.1109/ICASSP.2019.8683193

Citation

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

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https://urn.fi/URN:NBN:fi:aalto-202002031960

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[article-cris] Sähkötekniikan korkeakoulu / ELEC