A probabilistic least-mean-squares filter

Jesus Fernandez-Bes, Victor Elvira, Steven Van Vaerenbergh

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

Abstract

We introduce a probabilistic approach to the LMS filter. By means of an efficient approximation, this approach provides an adaptable step-size LMS algorithm together with a measure of uncertainty about the estimation. In addition, the proposed approximation preserves the linear complexity of the standard LMS. Numerical results show the improved performance of the algorithm with respect to standard LMS and state-of-the-art algorithms with similar complexity. The goal of this work, therefore, is to open the door to bring somemore Bayesian machine learning techniques to adaptive filtering.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages2199-2203
Number of pages5
Volume2015-August
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 1 Jan 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period19/04/1424/04/14

Keywords / Materials (for Non-textual outputs)

  • adaptive filtering
  • least-mean-squares
  • probabilisticmodels
  • state-space models

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