Abstract
In the framework of Independent Component Analysis (ICA), kurtosis has been used widely in designing source separation algorithms. In fact, the sum of absolute kurtosis values of all the output components is an effective objective function for separating arbitrary sources. In this paper, we propose an efficient ICA algorithm via a modified Jacobi optimization procedure on the kurtosis-sum objective function. The optimal rotation angle for any pair of the output components can be solved directly. It is demonstrated by numerical simulation experiments that our proposed algorithm can be even more computationally efficient than the FastICA algorithm under the same separation performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cardoso, J.F.: Blind Signal Separation: Statistical Principles. Proceedings of the IEEE 86, 2009–2025 (1998)
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)
Cardoso, J.F.: High-order contrasts for Independent Component Analysis. Neural Computation 11, 157–192 (1999)
Delfosse, N., Loubaton, P.: Adaptive Blind Separation of Independent Sources: a Deflation Approach. Signal Processing 45, 59–83 (1995)
Ge, F., Ma, J.: Analysis of the Kurtosis-Sum Objective Function for ICA. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008, Part I. LNCS, vol. 5263, pp. 579–588. Springer, Heidelberg (2008)
Moreau, E., Macchi, O.: High-order Contrast for Self-adaptive Source Separation. International Journal of Adaptive Control and Signal Processing 10, 19–46 (1996)
Hyvärinen, A.: Fast and Robust Fixed-point Algorithms for Independent Component Analysis. IEEE Trans. Neural Networks 10, 626–634 (1999)
Zarzoso, V., Nandi, A.K.: Blind Separation of Independent Sources for Virtually any Source Probability Density Function. IEEE Trans. Signal Processing 47, 2419–2432 (1999)
Zarzoso, V., Nandi, A.K., Herrmann, F., Millet-Roig, J.: Combined Estimation Scheme for Blind Source Separation with Arbitary Source PDFs. Electronic Letters 37, 132–133 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ge, F., Ma, J. (2010). An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-14831-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
eBook Packages: Computer ScienceComputer Science (R0)