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ICA is a statistical technique that decomposes a set of signals into spatial component maps of maximal statistical independence (Beckmann & Smith, 2004).
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The filters are learned from natural images using independent component analysis by maximizing the statistical independence of s i.
The unmixing matrix is found by maximizing the statistical independence of the unmixed signals.
The unmixing matrix is estimated by maximizing the statistical independence of the estimated components.
The main principle that the ICA depends on is the maximization of the statistical independence between the estimated components.
Within the statistical techniques, independent component analysis (ICA) [10, 11] assumes statistical independence among sources, while independent subspace analysis [12] extends ICA to single-channel source separation.
The FastICA algorithm is based on a fixed-point iteration scheme maximizing non-Gaussianity as a measure of statistical independence.
The essence behind independent component analysis is the assumption of statistical independence of the sources.
The statistics used took into account the lack of statistical independence of twin sisters.
In addition to the assumption of statistical independence between frequency bins, 2R+1 successive frames are also assumed to be independent.
where Ip stands for statistical independence.
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