Fast and effective model order selection method to determine the number of sources in a linear transformation model

European Signal Processing Conference

Volumn , Issue , 2012, Pages 1870-1874

  Cong, Fengyu ^a   Nandi, Asoke K ^a,b   He, Zhaoshui ^c   Cichocki, Andrzej ^d   Ristaniemi, Tapani ^a  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^d RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

Linear transformation model; model order selection; number of sources; ratio of adjacent eigenvalues; signal to noise ratio

Indexed keywords

EIGENVALUES; MODEL-ORDER SELECTION; SECOND ORDER STATISTICS;

EIGENVALUES AND EIGENFUNCTIONS; LINEAR TRANSFORMATIONS; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO;

COMPUTER SIMULATION;

EID: 84869852579     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. P. Stoica and Y. Selen, "Model-order selection: A review of information criterion rules," IEEE Signal Process, Mag., vol. 21, pp. 36-47, 2004.
2. M. H. Hansen and B. Yu, "Model selection and the principle of minimum description length," J. Am. Stat. Assoc., vol. 96, pp. 746-774, 2001.
3. P. M. T. Broersen and H. E. Wensink, "Autoregressive model order selection by a finite sample estimator for the Kullback-Leibler discrepancy," IEEE Trans. Signal Process., vol. 46, pp. 2058-2061, 1998.
4. Z. He, A. Cichocki, S. Xie and K. Choi, "Detecting the number of clusters in n-way probabilistic clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, pp. 2006-2021, 2010.
5. T. P. Minka, "Automatic choice of dimensionality for PCA," Advances in Neural Information Processing Systems, vol. 15, pp. 604, 2001.
6. A. Abou-Elseoud, T. Starck, J. Remes, J. Nikkinen, O. Tervonen and V. Kiviniemi, "The effect of model order selection in group PICA," Hum. Brain Mapp., vol. 31, pp. 1207-1216, 2010.
7. C. F. Beckmann and S. M. Smith, "Probabilistic independent component analysis for functional magnetic resonance imaging," IEEE Trans. Med. Imaging, vol. 23, pp. 137-152, 2004.
8. F. Cong, Z. He, J. Hämäläinen, A. Cichocki and T. Ristaniemi, "Determining the number of sources in high-density EEG recordings of event-related potentials by model order selection," in Proc. IEEE Workshop on Machine Learning for Signal Processing (MLSP) 2011, Beijing, China, 2011.
9. Y. O. Li, T. Adali and V. D. Calhoun, "Estimating the number of independent components for functional magnetic resonance imaging data," Hum. Brain Mapp., vol. 28, pp. 1251-1266, 2007.
10. H. Akaike, "A new look at statistical model identification," IEEE Trans. Autom. Control, vol. 19, pp. 716-723, 1974.
11. J. Rissanen, "Modeling by the shortest data description," Automatica, vol. 14, pp. 465-471, 1978.
12. J. E. Cavanaugh, " A large-sample model selection criterion based on Kullback's symmetric divergence," Stat. Probab. Lett., vol. 44, pp. 333-344, 1999.
13. A. P. Liavas and P. A. Regalia, "On the behavior of information theoretic criteria for model order selection " IEEE Tran. Signal Process., vol. 49, pp. 1689-1695, 2001.
14. G. Schwarz, " Estimating the dimension of a model," Ann. Stat., vol. 6, pp. 461-464, 1978.
15. A. Seghouane and A. Cichocki, "Bayesian estimation of the number of principal components," Signal Process., vol. 87, pp. 562-568, 2007.
16. S. M. Kay, A. H. Nuttall and P. M. Baggenstoss, "Multidimensional probability density function approximations for detection, classification, and model order selection " IEEE Trans. Signal Process., vol. 49, pp. 2240-2252, 2001.
17. M. O. Ulfarsson and V. Solo, "Rank selection in noisy PCA with sure and random matrix theory," in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008) Las Vegas, nevada, USA, 2008, pp. 3317-3320.
18. Y. Selen and E. G. Larsson, "Empirical bayes linear regression with unknown model order ," in 2007 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2007), Honolulu, Hawaii, USA, 2007, pp. 773-776.
19. W. Alkhaldi, D. R. Iskander and A. M. Zoubir, "Improving the performance of model-order selection criteria by partial-model selection search " in 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP2010), Dallas, Texas, USA, 2010, pp. 4130-4133.
20. Z. He, A. Cichocki and S. Xie, " Efficient method for Tucker3 model selection," Electron. Lett., vol. 45, pp. 805-806, 2009.
21. A. P. Liavas, P. A. Regalia and J. P. Delmas, "Blind channel approximation: Effective channel order determination," IEEE Trans. Signal Process., vol. 47, pp. 3336-3344, IEEE Transctions on Signal processing, 1999.