Canonical Correlation Analysis(CCA) is
a statistical analysis technique to identify correlations between two sets of
variables. Given two vector variables X and Y, it finds two projections,
one for each, to transform them to a common space with maximum correlations.
The package defines a CCA type to represent a CCA model, and provides a set of methods to access the properties.
CCA
Let M be an instance of CCA, dx be the dimension of X,
dy the dimension of Y, and p the output dimension (i.e the dimension of the common space).
fit(::Type{CCA}, ::AbstractMatrix{T}, ::AbstractMatrix{T}) where {T<:Real}
size(::CCA)
mean(::CCA, ::Symbol)
projection(::CCA, ::Symbol)
cor(::CCA)
predict(::CCA, ::AbstractVecOrMat{<:Real}, ::Symbol)
Auxiliary functions:
ccacov
ccasvd