Regularized LMS and diffusion adaptation LMS with graph filters for non-stationary data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In sensor networks, adaptive algorithms such as diffusion adaptation LMS are commonly used to learn and track non-stationary signals. When such signals have similarities across certain nodes as captured by a graph, then Laplacian Regularized (LR) LMS and diffusion adaptation LR LMS can be utilized for the respective centralized and distributed estimation cases. In this paper, we re-examine these adaptive methods, and use graph signal processing notions to augment the algorithms with an additional graph filtering step for regularization. Moreover, we demonstrate how to design these graph filters, leading to performance improvements over existing methods in both the centralized and distributed cases. Furthermore, we analyze the stability and convergence of our methods and illustrate how the empirical performance is captured by the theoretical results which unveil the bias and variance tradeoff.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2017-December
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Other

Other7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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  • Cite this

    Lin, M. Z., Murthi, M., & Premaratne, K. (2018). Regularized LMS and diffusion adaptation LMS with graph filters for non-stationary data. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 (Vol. 2017-December, pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2017.8313093