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Principal component analysis
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=== Correspondence analysis === [[Correspondence analysis]] (CA) was developed by [[Jean-Paul Benzécri]]<ref>{{Cite book | author = Benzécri, J.-P. | publisher=Dunod |location= Paris, France | year = 1973 | title = L'Analyse des Données. Volume II. L'Analyse des Correspondances }}</ref> and is conceptually similar to PCA, but scales the data (which should be non-negative) so that rows and columns are treated equivalently. It is traditionally applied to [[contingency tables]]. CA decomposes the [[chi-squared statistic]] associated to this table into orthogonal factors.<ref>{{Cite book | author = Greenacre, Michael | publisher=Academic Press |location= London | year = 1983 | title = Theory and Applications of Correspondence Analysis | isbn = 978-0-12-299050-2 }}</ref> Because CA is a descriptive technique, it can be applied to tables for which the chi-squared statistic is appropriate or not. Several variants of CA are available including [[detrended correspondence analysis]] and [[canonical correspondence analysis]]. One special extension is [[multiple correspondence analysis]], which may be seen as the counterpart of principal component analysis for categorical data.<ref>{{Cite book |author1=Le Roux |author2=Brigitte and Henry Rouanet | publisher=Kluwer|location= Dordrecht | year = 2004 | title = Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis | isbn =9781402022357 | url=https://books.google.com/books?id=a6bDBUF58XwC }}</ref>
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