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We study and derive a method to speed up kurtosis-based FastICA in presence of information redundancy, i.e., for large samples. It consists in randomly decimating the data set as more as possible while preserving the quality of the reconstructed signals. By performing an analysis of the kurtosis estimator, we find the maximum reduction rate which guarantees a narrow confidence interval of such estimator with high confidence level. Such a rate depends on a parameter β easily computed a prioridoi:10.1007/978-3-540-74494-8_24 dblp:conf/ica/GaitoG07 fatcat:d72ohkiizrcrrppaerx3dntm3i