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Independent component analysis - Wikipedia
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation.A common example application is the "cocktail party problem ...
 Independent Component Analysis: A Review
analysis, projection pursuit and factor analysis. So Independent Component Analysis (ICA) is a method with help of which we can have a linear representation of nongaussian data so that the components are statistically independent. So, in this paper we see the basic theory and application of ICA. Key words: linear transformations makes ...
ICA for dummies – Arnaud Delorme
Independent Component Analysis is a signal processing method to separate independent sources linearly mixed in several sensors. For instance, when recording electroencephalograms (EEG) on the scalp, ICA can separate out artifacts embedded in the data (since they are usually independent of each other).
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