# canonical correlation analysis

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## canonical correlation analysis

A statistical manoeuvre which defines covariation for 2 groups of random variables—x and y—and seeks to identify linear combinations of the x's and the y's which have maximum correlation with each other.
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The first canonical correlation analysis considered the relationship between athletes' likelihood of reporting concussion symptoms to the coach in a regular or big game with athletes' perceptions of the climate.
Canonical correlation was used to examine the relationship between coping and psychological help-seeking attitudes and intentions (Guarino, 2004; Sherry & Henson, 2005).
The results show that three pairs of canonical correlation variates are significant in the relationship between ego identity and referral program involvement.
To test the hypotheses, we conducted a canonical correlation analysis (Tabachnick & Fidell, 2013), with the ICM skills specified as loading on the skills variates, and the ICM outcomes specified as loading on the outcomes variates.
The Canonical Correlation Analysis (CCA) is used to improve Image Retrieval using visual and textual data of an image.
Results of the canonical correlation analysis are summarized in Table 2.
In the canonical correlation analysis, the first two canonical variables explained approximately 89.73% of the total available variation in the data (73.74% for the first canonical variable, 15.99% for the second) (Table 2).
For the first canonical function, results yielded maximum canonical correlation coefficient (between the linear combinations of predictor and criterion variables) = .585 with squared canonical correlation = .342.
The orthogonal regularization canonical correlation analysis (ORCCA) algorithm  is that the original formula of CCA algorithm with orthogonal constraints is substituted for CCA conjugate orthogonalization [6, 7].
where [W.sub.M] and [W.sub.N] are two transformations that maximize the canonical correlation [rho] between canonical variables U and V.
In this study, the canonical correlation between CAD and metabolic pathways gene expressions was analyzed.
For such a purpose, the authors propose an effective fusion framework that uses a Kernel-based Canonical Correlation Analysis (KCCA).

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