Abstract
Principal components (PCs) by construction have a natural ordering based on their cumulative proportion of variance explained. However, most ICA algorithms for finding independent components (ICs) are arbitrary, which limit the use of ICA in pattern discovery and dimension reduction. To solve this problem, we propose an efficient IC ordering approach and prove that this method guarantees to find the optimal ordering of ICs based on the MSE criterion. Furthermore, we employ the cross validation method to select the number of dominant ICs. Simulation experiments show that the proposed IC ordering and selection procedure is efficient and effective, which can be used to identify the dominant ICs as well as to reduce the number of ICs.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cheung, Y.-m., Xu, L.: An Empirical Method to Select Dominant Independent Components in ICA for Time Series Analysis. In: Proceedings of 1999 International Joint Conference on Neural Networks (IJCNN 1999), Washington, DC, July 10-16, vol. 6, pp. 3883–3887 (1999)
Cheung, Y.-m., Xu, L.: Independent Component Ordering in ICA Time Series Analysis. Neurocomputing 41(1-4), 145–152 (2001)
Alexander, C.: Market Models: A Guide to Financial Data Analysis. J. Wiley, UK (2001)
Comon, P.: Independent component analysis: a new concept? Signal Processing 36, 287–314 (1994)
Craven, P., Wahba, G.: Smoothing noisy data with spline functions. Numer. Math. 31, 377–403 (1979)
Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56 (1993)
Golub, G., Heath, M., Wahba, G.: Generalized cross validation as a method for choosing a good ridge parameter. Technometrics 21, 215–224 (1979)
Heath, D., Jarrow, R., Morton, A.: Bond Pricing and the Term Structure of Interest Rates: A New Methodology. Econometrica 60(1), 77–105 (1992)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. J. Wiley, New York (2001)
Jutten, C., Herault, J.: Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1–10 (1991)
Wu, E.H.C., Yu, P.L.H.: Volatility modelling of multivariate financial time series by using ICA-GARCH models. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 571–579. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, E.H., Yu, P.L.H., Li, W.K. (2006). An Independent Component Ordering and Selection Procedure Based on the MSE Criterion. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_36
Download citation
DOI: https://doi.org/10.1007/11679363_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)