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On the rank-deficient canonical correlation technique solved by analytic spectral decomposition

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  • Lukáš Malec

Abstract

Regularization is a well-known and used statistical approach covering individual points or limit approximations. In this study, the canonical correlation analysis (CCA) process of the paths is discussed with partial least squares (PLS) as the other boundary covering transformation to a symmetric eigenvalue (or singular value) problem dependent on a parameter. Two regularizations of the original criterion in the parameterization domain are compared, i.e. using projection and by identity matrix. We discuss the existence and uniqueness of the analytic path for eigenvalues and corresponding elements of eigenvectors. Specifically, canonical analysis is applied to an ill-conditioned case of singular within-sets input matrices encompassing tourism accommodation data.

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  • Lukáš Malec, 2022. "On the rank-deficient canonical correlation technique solved by analytic spectral decomposition," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(4), pages 819-830, March.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:819-830
    DOI: 10.1080/02664763.2020.1843608
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