Editing Independent component analysis (section)

{{Short description|Signal processing computational method}}
{{More citations needed|date=October 2011}}
{{machine learning|Dimensionality reduction}}
In [[signal processing]], '''independent component analysis''' ('''ICA''') is a computational method for separating a [[multivariate statistics|multivariate]] signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are [[Statistical independence|statistically independent]] from each other.<ref>{{cite web | url=http://research.ics.aalto.fi/ica/icademo/ | title=Independent Component Analysis: A Demo }}</ref> ICA was invented by Jeanny Hérault and Christian Jutten in 1985.<ref name=jutten85/> ICA is a special case of [[blind source separation]]. A common example application of ICA is the "[[cocktail party problem]]" of listening in on one person's speech in a noisy room.<ref>{{Cite journal|last=Hyvärinen|first=Aapo|date=2013|title=Independent component analysis: recent advances|journal=Philosophical Transactions: Mathematical, Physical and Engineering Sciences|volume=371|issue=1984|pages=20110534|issn=1364-503X|jstor=41739975|bibcode=2012RSPTA.37110534H|doi=10.1098/rsta.2011.0534|pmid=23277597|pmc=3538438}}</ref>