kcca {kernlab} | R Documentation |
Computes the canonical correlation analysis in a feature space.
## S4 method for signature 'matrix': kcca(x, y, kernel="rbfdot", kpar=list(sigma=0.1), ...)
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 setting the kernel parameter to the following
strings:
|
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 :
Hyper-parameters for user defined kernels can be passed through the kpar parameter as well. |
... |
adittional parameters for the kpca function |
The kernel version of canonical correlation analysis.
An S4 object containg the following slots:
kcor |
Correlation coefficients in feature space |
xcoef |
estimated coefficients for the x variables in the
feature space |
ycoef |
estimated coefficients for the y variables in the
feature space |
xvar |
The canonical variates for x |
yvar |
The canonical variates for y |
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
Malte Kuss, Thore Graepel
The Geometry Of Kernel Canonical Correlation Analysis
http://www.kyb.tuebingen.mpg.de/publications/pdfs/pdf2233.pdf