A sliding-window online fast variational sparse Bayesian learning algorithm

Thomas Buchgraber, Dmitriy Shutin, H. Vincent Poor
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
more » ... scribed 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.
doi:10.1109/icassp.2011.5946747 dblp:conf/icassp/BuchgraberSP11 fatcat:3wzxop3vszejlnvdyrmostp3j4