Salīdzināt metodes

	Biplots: vienlaicīga rindu un kolonnu attēlošana daudzdimensiju datos	Daudzveidu atbilstības analīze (MCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1971	2006
Autors	Ruben Gabriel	Greenacre & Blasius
Tips	Multivariate graphical display	Multivariate exploratory ordination
Pirmavots	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.	Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1-58488-628-0
Citi nosaukumi	Gabriel biplot, PCA biplot, JK biplot, Çift grafik	MCA, Homogeneity Analysis, Multiple Nominal Component Analysis, Çoklu Uyum Analizi
Saistītās	2	2
Kopsavilkums	A biplot is a low-dimensional graphical representation of a multivariate data matrix that simultaneously displays both the observations (rows) and the variables (columns) as points or vectors in the same plot. Introduced by Ruben Gabriel in 1971, the technique decomposes the data matrix into a rank-2 approximation using singular value decomposition, allowing the approximate value of any data entry to be read as the inner product of the corresponding row and column markers.	Multiple Correspondence Analysis (MCA) is a multivariate ordination technique designed to explore and visualize associations among three or more categorical variables simultaneously. By mapping both observations and variable categories onto a shared low-dimensional space, MCA reveals hidden structure in nominal or ordinal survey data. The method was comprehensively systematized and extended by Michael Greenacre and Jorg Blasius in their 2006 edited volume, building on earlier geometric data analysis traditions developed in France by Jean-Paul Benzecri during the 1960s and 1970s.