方法对比

	多维尺度分析 (MDS)	对应分析
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	1952–1964	1984
提出者	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)	Jean-Paul Benzécri; Michael Greenacre
类型	Dimensionality reduction / visualization	Exploratory multivariate technique for categorical data
开创性文献	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
别名	MDS, metric MDS, non-metric MDS, proximity scaling	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
相关	5	2
摘要	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.	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.