# Canonical Correlation Analysis Task

A Canonical Correlation Analysis Task is a correlations analysis task that ...

**AKA:**CCA.**Context:**- It can be solved by a Canonical Correlation Analysis System (that implements a Canonical Correlation Analysis Algorithm).

**Counter-Example(s):****See:**Cross-Covariance Matrix, Random Variable, Correlation, Parametric Statistics.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Canonical_correlation Retrieved:2015-4-30.
- In statistics,
**canonical-correlation analysis**(**CCA**) is a way of making sense of cross-covariance matrices. If we have two vectors*X*= (XX_{1}, ...,n) and_{}*Y*= (*Y*_{1}, ...,*Y*_{m}) of random variables, and there are correlations among the variables, then canonical-correlation analysis will find linear combinations of the*X*_{i}and*Y*_{j}which have maximum correlation with each other. T. R. Knapp notes "virtually all of the commonly encountered parametric tests of significance can be treated as special cases of canonical-correlation analysis, which is the general procedure for investigating the relationships between two sets of variables." The method was first introduced by Harold Hotelling in 1936.

- In statistics,

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Canonical_correlation#Practical_uses Retrieved:2015-4-30.
- A typical use for canonical correlation in the experimental context is to take two sets of variables and see what is common amongst the two sets. For example in psychological testing, you could take two well established multidimensional personality tests such as the Minnesota Multiphasic Personality Inventory (MMPI-2) and the NEO. By seeing how the MMPI-2 factors relate to the NEO factors, you could gain insight into what dimensions were common between the tests and how much variance was shared. For example you might find that an extraversion or neuroticism dimension accounted for a substantial amount of shared variance between the two tests.
One can also use canonical-correlation analysis to produce a model equation which relates two sets of variables, for example a set of performance measures and a set of explanatory variables, or a set of outputs and set of inputs. Constraint restrictions can be imposed on such a model to ensure it reflects theoretical requirements or intuitively obvious conditions. This type of model is known as a maximum correlation model. Visualization of the results of canonical correlation is usually through bar plots of the coefficients of the two sets of variables for the pairs of canonical variates showing significant correlation. Some authors suggest that they are best visualized by plotting them as heliographs, a circular format with ray like bars, with each half representing the two sets of variables.

- A typical use for canonical correlation in the experimental context is to take two sets of variables and see what is common amongst the two sets. For example in psychological testing, you could take two well established multidimensional personality tests such as the Minnesota Multiphasic Personality Inventory (MMPI-2) and the NEO. By seeing how the MMPI-2 factors relate to the NEO factors, you could gain insight into what dimensions were common between the tests and how much variance was shared. For example you might find that an extraversion or neuroticism dimension accounted for a substantial amount of shared variance between the two tests.

### 2008

- (Upton & Cook, 2008)
- QUOTE: Method of assessing the relationship between two groups of variables (for example, the relationship between three measures of a worker’s ability and four measures of his or her performance).

### 2004

- (Hardoon et al., 2004) ⇒ David R. Hardoon, Sandor R. Szedmak, and John R. Shawe-taylor. (2004). “Canonical Correlation Analysis: An Overview with Application to Learning Methods.” In: Neural Computation Journal, 16(12). doi:10.1162/0899766042321814