Abstract
In this chapter, we treat procedures for the data set in which variables are classified into some groups. Such a data set is expressed as a block matrix , introduced in Sect. 14.1. Then, we describe canonical correlation analysis (CCA) for data with two groups of variables, which is followed by the introduction of generalized CCA (GCCA) for more than two groups of variables in Sect. 14.3. GCCA provides a foundation for a procedure analyzing the multivariate categorical data described in Sect. 14.4.
The original version of this chapter was revised: Belated corrections have been incorporated. The erratum to this chapter is available at https://doi.org/10.1007/978-981-10-2341-5_17
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Adachi, K. (2016). Canonical Correlation and Multiple Correspondence Analyses. In: Matrix-Based Introduction to Multivariate Data Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-2341-5_14
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DOI: https://doi.org/10.1007/978-981-10-2341-5_14
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