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Independent component analysis (ICA) is a way to get certain hidden information out of a signal. Experts define it as a “multivariate data model” that brings non-Gaussian and mutually independent elements out of a combined signal.
The process of independent component analysis can be applied to many different types of data sources. Generally, these are pulled together from independent sources into the group, as in the case of digital images, biofeedback information, radio signals or sound streams for microphones. Some experts talk about the “cocktail party problem” where analysts separate out a single voice in a crowd of people on the street. In these types of situations, the use of independent component analysis can be effective where other traditional methods are not.