Distributed recursive least-squares with data-adaptive censoring

Zifeng Wang, Zheng Yu, Qing Ling, Dimitris Berberidis, Georgios B Giannakis

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

3 Scopus citations

Abstract

The deluge of networked big data motivates the development of computation- and communication-efficient network information processing algorithms. In this paper, we propose two data-adaptive censoring strategies that significantly reduce the computation and communication costs of the distributed recursive least-squares (DRLS) algorithm. Through introducing a cost function that underrates the importance of those observations with small innovations, we develop the first censoring strategy based on the alternating minimization algorithm and the stochastic Newton method. It saves computation when a datum is censored. The computation and communication costs are further reduced by the second censoring strategy, which prohibits a node updating and transmitting its local estimate to neighbors when its current innovation is less than a threshold. For both strategies, a simple criterion for selecting the threshold of innovation is given so as to reach a target ratio of data reduction. The proposed censored D-RLS algorithms guarantee convergence to the optimal argument in the mean-square deviation sense. Numerical experiments validate the effectiveness of the proposed algorithms.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5860-5864
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Distributed networks
  • data-adaptive censoring
  • distributed recursive least-squares (D-RLS)

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