Abstract
Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects.
This work was supported by the Natural Science Foundation of China for Project 60471054.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tong, L., Inouye, Y., Liu, R.: Waveform-preserving blind estimation of multiple independent sources. IEEE Trans. on Signal Processing 41, 2461–2470 (1993)
Comon, P.: Independent component analysis–a new concept? Signal Processing 36, 287–314 (1994)
Bell, A., Sejnowski, T.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Amari, S.I., Cichocki, A., Yang, H.: A new learning algorithm for blind separation of sources. Advances in Neural Information Processing 8, 757–763 (1996)
Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans. on Neural Networks 10, 626–634 (1999)
Xu, L., Cheung, C.C., Amari, S.I.: Learned parametric mixture based ica algorithm. Neurocomputing 22, 69–80 (1998)
Xu, L., Cheung, C.C., Amari, S.I.: Further results on nonlinearity and separation capability of a linear mixture ICA method and learned LPM. In: Fyfe, C. (ed.) Proceedings of the I&ANN 1998, pp. 39–45 (1998)
Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 11, 417–441 (1999)
Welling, M., Weber, M.: A constrained EM algorithm for independent component analysis. Neural Computation 13, 677–689 (2001)
Liu, Z.Y., Chiu, K.C., Xu, L.: One-bit-matching conjecture for independent component analysis. Neural Computation 16, 383–399 (2004)
Ma, J., Liu, Z., Xu, L.: A further result on the ICA one-bit-matching conjecture. Neural Computation 17, 331–334 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Ge, F., Gao, D. (2005). Two Adaptive Matching Learning Algorithms for Independent Component Analysis. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_136
Download citation
DOI: https://doi.org/10.1007/11596448_136
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)