Abstract
Multiple correspondence analysis (MCA) is a widely used technique to analyze categorical data and aims to reduce large sets of variables into smaller sets of components that summarize the information contained in the data. The purpose of MCA is the same as that of principal component analysis (PCA), and MCA can be regarded as an adaptation to the categorical data of PCA (Jolliffe, Principal Component Analysis, 2002). There are various approaches to formulate an MCA. We introduce a formulation in which the quantified data matrix is approximated by a lower-rank matrix using the quantification technique proposed by Murakami et al. (Non-metric principal component analysis for categorical variables with multiple quantifications, 1999).
The original version of this chapter was revised: Typos were corrected throughout the chapter. The erratum to this chapter is available at http://dx.doi.org/10.1007/978-981-10-0159-8_8.
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Mori, Y., Kuroda, M., Makino, N. (2016). Multiple Correspondence Analysis. In: Nonlinear Principal Component Analysis and Its Applications. SpringerBriefs in Statistics(). Springer, Singapore. https://doi.org/10.1007/978-981-10-0159-8_3
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DOI: https://doi.org/10.1007/978-981-10-0159-8_3
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