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A sliding-window online fast variational sparse Bayesian learning algorithm
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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
doi:10.1109/icassp.2011.5946747
dblp:conf/icassp/BuchgraberSP11
fatcat:3wzxop3vszejlnvdyrmostp3j4