Abstract
In this paper, we propose to use the Huber M-estimator cost function as a contrast function within the complex FastICA algorithm of Bingham and Hyvarinen for the blind separation of mixtures of independent, non-Gaussian, and proper complex-valued signals. Sufficient and necessary conditions for the local stability of the complex-circular FastICA algorithm for an arbitrary cost are provided. A local stability analysis shows that the algorithm based on the Huber M-estimator cost has behavior that is largely independent of the cost function’s threshold parameter for mixtures of non-Gaussian signals. Simulations demonstrate the ability of the proposed algorithm to separate mixtures of various complex-valued sources with performance that meets or exceeds that obtained by the FastICA algorithm using kurtosis-based and other contrast functions.
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References
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Chao, JC., Douglas, S.C. (2007). A Robust Complex FastICA Algorithm Using the Huber M-Estimator Cost Function. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_20
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DOI: https://doi.org/10.1007/978-3-540-74494-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
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