Neuromorphic Engineering
Independent Component Analysis (ICA) is a computational technique used to separate a multivariate signal into additive, independent non-Gaussian components. This method is particularly useful in unsupervised learning contexts, as it helps to discover hidden factors that underlie observed data without prior labeling. ICA assumes that the observed signals are linear mixtures of the independent sources and aims to reconstruct these original signals by maximizing statistical independence.
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