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.
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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
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DOI: https://doi.org/10.1007/978-3-642-14831-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
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