• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.305-313
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• 2019

Generalized canonical correlation analysis (GCCA) extends the canonical correlation analysis (CCA) to the case of more than two sets of variables and there have been many studies on how two-set canonical solutions can be generalized. In this paper, we derive certain stationary equations which can lead the higher-order solutions of several GCCA methods and suggest a type of iterative procedure to obtain the canonical coefficients. In addition, with some numerical examples we present the methods for graphical display, which are useful to interpret the GCCA results obtained.

• Asian-Australasian Journal of Animal Sciences
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• 제21권11호
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• pp.1576-1584
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• 2008

In this study, canonical correlation analysis (CCA) was applied to estimate the relationship between three different sexual maturity traits (X set: days to first egg (DFE), weight of the first egg (WFE), body weight at first egg (BWFE)) and level of nutrient intake (Y set: energy (EI) and protein intake (PI)) or the egg production traits at two different periods (Z set: number of egg (NE1 and NET) and weight of egg (WE1 and WET) from 22 to 25 (Wfirst) and 22 to 33 wk of age (Wall), respectively), which were measured from 64 egg-type pullets (Isa Brown) manipulated for time of access to energy and protein sources to onset of egg production. Partial CCA (PCCA) was used to eliminate the contribution of differences in the levels of nutrient intake to canonical variables for X and Z sets at the first production period. Estimated canonical correlation coefficients between X set and Y set (0.429, p = 0.042), X set and Z set (0.390, p = 0.007 for Wfirst) and within Z set (between Wfirst and Wall; 0.780, p<0.001), and partial canonical correlation coefficient between X set and Z set (0.415, p = 0.009) were significant. Canonical weights and loadings from CCA indicated that the BWFE had the largest contribution compared to the DFE and WFE to variation of egg number produced at two different periods. The results from PCCA indicated that the contribution of PI and EI to the degree of the correlation between canonical variables for X and Z sets were unfavourable. In conclusion, the effect of body weight at sexual maturity upon the availability of nutrients can have a higher contribution to variation of egg production in pullets if the contribution of differences in nutrient intakes to onset of egg production were eliminated.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.