@inproceedings{cfa1cb8e5ae54583a6c10e499edfbd37,
title = "A sliding-window online fast variational sparse Bayesian learning algorithm",
abstract = "In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. A sequential decision rule for inclusion or deletion of basis functions is obtained by applying a recently proposed fast variational sparse Bayesian learning (SBL) method. The proposed scheme uses a sliding window estimator to process the data in an online fashion. The noise variance can be implicitly estimated by the algorithm. It is shown that the described method has better mean square error (MSE) performance than a state of the art kernel recursive least squares (Kernel-RLS) algorithm when using the same number of basis functions.",
keywords = "Variational inference, online learning, sparse Bayesian learning",
author = "Thomas Buchgraber and Dmitriy Shutin and Poor, {H. Vincent}",
year = "2011",
doi = "10.1109/ICASSP.2011.5946747",
language = "English (US)",
isbn = "9781457705397",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "2128--2131",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",
note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
}