Independent component analysis
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 at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.
Independent component analysis - Wikipedia Jump to content From Wikipedia, the free encyclopedia Signal processing computational method This article needs more citations . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed . Find sources: "Independent component analysis" – news · newspapers · books · scholar · JSTOR ( October 2011 ) ( Learn how and when to remove this message ) Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised
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