2015
DOI: 10.1080/00949655.2015.1110821
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Some remarks on the functional relation between canonical correlation analysis and partial least squares

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Cited by 9 publications
(5 citation statements)
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“…In comparison to canonical correlation analysis, the PLS algorithm ranges to define a new coordinate system so that such a pair of coordinate systems is optimal in maximizing the covariance of individual variables of the first data set to LV of the second set (Wold, 1975). Malec (2016) discusses the functional relation for both PLS and canonical analysis. While in the case of PLS-LDA, the distances between LVs are to be maximized, for partial least squares, the covariance relations are in the area of interest.…”
Section: Methodsmentioning
confidence: 99%
“…In comparison to canonical correlation analysis, the PLS algorithm ranges to define a new coordinate system so that such a pair of coordinate systems is optimal in maximizing the covariance of individual variables of the first data set to LV of the second set (Wold, 1975). Malec (2016) discusses the functional relation for both PLS and canonical analysis. While in the case of PLS-LDA, the distances between LVs are to be maximized, for partial least squares, the covariance relations are in the area of interest.…”
Section: Methodsmentioning
confidence: 99%
“…Specifically, in this study, the functional relation examines the analytical (thus also smooth) paths of eigenvalues and corresponding eigenvectors solving the Fisher discriminant analysis (Malec, 2015). Malec (2016) compares canonical correlation analysis and partial least squares considering also their kernelized versions which relation was derived in a similar way as among discriminant analysis methods used in this study. Some preliminary notes are also mentioned in .…”
Section: Methodsmentioning
confidence: 99%
“…Fisher linear discriminant analysis (also the so called Fisher discriminant analysis) and principal component analysis (PCA) supplemented by descriptive statistic outputs. Fisher discriminant analysis was extended by analytical paths of eigenvalues and eigenvectors to originate the other boundary point corresponding to its partial least squares variant derived likewise the connection of canonical correlation analysis and partial least squares (Malec, 2016) with various statistical properties of its boundaries, see e.g. the studies by Wegelin (2000) or Malec (2013).…”
Section: Introductionmentioning
confidence: 99%
“…To evaluate economic data, various statistical methods, including multidimensional ones, are commonly employed; see, e.g., Malec (2016) or Malec and Janovský (2019). In this research, standard analytical methods were used to model time series and construct predictions.…”
Section: Introductionmentioning
confidence: 99%