e/Multiple correspondence analysis

Information
has gloss	eng: In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal or categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space. The procedure thus appears to be the counterpart of principal component analysis for categorical data . MCA is an extension of simple correspondence analysis (CA) in that it is appliable to a large set of variables. Instead of analysing the contingency table (or cross-tabulation), as CA does, MCA analyzes an indicator matrix. An indicator matrix is an Individuals × Variables matrix, where the rows represent individuals and the columns represent categories of the variables. . Analyzing the indicator matrix allows the representation of individuals as points in geometric space.
lexicalization	eng: Multiple correspondence analysis
instance of	e/Multivariate statistics