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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Adachi, K. (2020). Canonical Correlation and Multiple Correspondence Analyses. In: Matrix-Based Introduction to Multivariate Data Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-15-4103-2_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-4103-2_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4102-5
Online ISBN: 978-981-15-4103-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)