Abstract
In light of the recently proposed generalized correlation function named correntropy, which exploits higher-order statistics and the time structure of signals, we have, in this work, two main objectives: 1) to give a new interpretation – founded on the relationships between the constant modulus (CM) and Shalvi-Weinstein criteria and between the latter and methods for ICA based on nongaussianity – to the performance of the constant modulus approach under dependent sources and 2) to analyze the correntropy in the context of blind deconvolution of i.i.d. and dependent sources, as well as to establish elements of a comparison between it and the CMA. The analyses and simulation results unveil some theoretical aspects hitherto unexplored.
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Neves, A., Wada, C., Suyama, R., Attux, R., Romano, J.M.T. (2009). An Analysis of Unsupervised Signal Processing Methods in the Context of Correlated Sources. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_11
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DOI: https://doi.org/10.1007/978-3-642-00599-2_11
Publisher Name: Springer, Berlin, Heidelberg
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