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 language | English |
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Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 2199-2203 |
Number of pages | 5 |
Volume | 2015-August |
ISBN (Electronic) | 9781467369978 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
Event | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 19 Apr 2014 → 24 Apr 2014 |
Conference
Conference | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 |
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Country/Territory | Australia |
City | Brisbane |
Period | 19/04/14 → 24/04/14 |
Keywords / Materials (for Non-textual outputs)
- adaptive filtering
- least-mean-squares
- probabilisticmodels
- state-space models