High-SNR model order selection using exponentially embedded family and its applications to curve fitting and clustering
Document Type
Conference Proceeding
Date of Original Version
1-13-2015
Abstract
The exponentially embedded family (EEF) of probability density functions was originally proposed in [1] for model order selection. The performance of the original EEF deteriorates somewhat when nuisance parameters are present, especially in the case of high signal-to-noise ratio (SNR). Therefore, we propose a new EEF for model order selection in the case of high SNR. It is shown that without nuisance parameters, the new EEF is the same as the original EEF. However, with nuisance parameters, the new EEF takes a different form. The new EEF is applied to problems of polynomial curve fitting and clustering. Simulation results show that, with nuisance parameters, the new EEF outperforms the original EEF and Bayesian information criterion (BIC) at high SNR.
Publication Title, e.g., Journal
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
Citation/Publisher Attribution
Ding, Quan, Steven Kay, and Xiaorong Zhang. "High-SNR model order selection using exponentially embedded family and its applications to curve fitting and clustering." IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings (2015): 495-500. doi: 10.1109/CIDM.2014.7008708.