Abstract
Independent component analysis (ICA) has many practical applications in the fields of signal and image processing and several ICA learning algorithms have been constructed via the selection of model probability density functions. However, there is still a lack of deep mathematical theory to validate these ICA algorithms, especially for the general case that super- and sub-Gaussian sources coexist. In this paper, according to the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, we propose a one-bit-matching ICA learning algorithm on the Stiefel manifold. It is shown by the simulated and audio experiments that our proposed learning algorithm works efficiently on the ICA problem with both super- and sub-Gaussian sources and outperforms the extended Infomax and Fast-ICA algorithms.
This work was supported by the Natural Science Foundation of China for Project 60471054.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tong, L., Inouye, Y., Liu, R.: Waveform-preserving blind estimation of multiple independent sources. IEEE Trans. Signal Processing 41(7), 2461–2470 (1993)
Comon, P.: Independent component analysis–a new concept? Signal Processing 36(3), 287–314 (1994)
Bell, A., Sejnowski, T.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 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)
Cardoso, J.F., Laheld, B.: Equivalent adaptive source separation. IEEE Trans. Signal Processing 44(12), 3017–3030 (1996)
Lee, T.W., Girolami, M., Sejnowski, T.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 11(2), 417–441 (1999)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10(3), 626–634 (1999)
Xu, L., Cheung, C.C., Amari, S.I.: Learned parametric mixture based ICA algorithm. Neurocomputing 22, 69–80 (1998)
Liu, Z.Y., Chiu, K.C., Xu, L.: One-bit-matching conjecture for independent component analysis. Neural Computation 16(2), 383–399 (2004)
Ma, J., Liu, Z., Xu, L.: A further result on the ICA one-bit-matching conjecture. Neural Computation 17(2), 331–334 (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
Ma, J., Gao, D., Ge, F., Amari, Si. (2006). A One-Bit-Matching Learning Algorithm for Independent Component Analysis. 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_22
Download citation
DOI: https://doi.org/10.1007/11679363_22
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)