Abstract
In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion. Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online.
Original language | English |
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Title of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 |
Number of pages | 5 |
Publisher | IEEE |
Publication date | May 2014 |
Pages | 1891 - 1895 |
ISBN (Electronic) | 978-1-4779-2892-7 |
DOIs | |
Publication status | Published - May 2014 |
Event | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) - Firenze, Italy Duration: 4 May 2014 → 9 May 2014 Conference number: 18874 |
Conference
Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
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Number | 18874 |
Country/Territory | Italy |
City | Firenze |
Period | 04/05/2014 → 09/05/2014 |
Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
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ISSN | 1520-6149 |