Independent Component Analysis (ICA) is a computational technique used to separate a multivariate signal into additive, independent components. This method assumes that the observed signals are mixtures of non-Gaussian source signals and aims to recover the original sources based on their statistical properties. ICA is widely used in fields such as signal processing and machine learning, particularly for tasks like source separation where different sources need to be identified and extracted from mixed data.
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