Сравнение методов

	Корреспондентский анализ	Многомерное шкалирование (MDS)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1984	1952–1964
Автор метода	Jean-Paul Benzécri; Michael Greenacre	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
Тип	Exploratory multivariate technique for categorical data	Dimensionality reduction / visualization
Основополагающий источник	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
Другие названия	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi	MDS, metric MDS, non-metric MDS, proximity scaling
Связанные	2	5
Сводка	Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.	Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.