Computes the canonical correlation analysis in a feature space.
Usage
## S3 method for class 'matrix':
kcca(x, y, kernel="rbfdot", kpar=list(sigma=0.1), ...)
Arguments
x
a matrix containing data index by row
y
a matrix containing data index by row
kernel
the kernel function used in training and predicting.
This parameter can be set to any function, of class kernel, which computes a dot product between two
vector arguments. kernlab provides the most popular kernel functions
which can be used by
kpar
the list of hyper-parameters (kernel parameters).
This is a list which contains the parameters to be used with the
kernel function. For valid parameters for existing kernels are :
sigmainverse kernel width for the Radial B
...
adittional parameters for the kpca function
Value
An S4 object containg the following slots:
kcorCorrelation coefficients in feature space
xcoefestimated coefficients for the x variables in the
feature space
ycoefestimated coefficients for the y variables in the
feature space
xvarThe canonical variates for x
yvarThe canonical variates for y
Details
The kernel version of canonical correlation analysis.
References
Malte Kuss, Thore Graepel
The Geometry Of Kernel Canonical Correlation Analysishttp://www.kyb.tuebingen.mpg.de/publications/pdfs/pdf2233.pdf