Abstract
n this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive non linear 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 re cursive least squares (Kernel-RLS) algorithm when using the same number of basis functions.
Original language | English |
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Title of host publication | IEEE International Conference on Acoustics, Speech, and Signal Processing |
Pages | 2128-2131 |
DOIs | |
Publication status | Published - 2011 |
Event | 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2011 - Prag, Czech Republic Duration: 22 May 2011 → 27 May 2011 |
Conference
Conference | 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Country/Territory | Czech Republic |
City | Prag |
Period | 22/05/11 → 27/05/11 |
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
- Theoretical
- Experimental