kcca {kernlab} | R Documentation |
Kernel Canonical Correlation Analysis
Description
Computes the canonical correlation analysis in feature space.
Usage
## S4 method for signature 'matrix'
kcca(x, y, kernel="rbfdot", kpar=list(sigma=0.1),
gamma = 0.1, ncomps = 10, ...)
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 inner product in feature space between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. |
kpar |
the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :
Hyper-parameters for user defined kernels can be passed through the kpar parameter as well. |
gamma |
regularization parameter (default : 0.1) |
ncomps |
number of canonical components (default : 10) |
... |
additional parameters for the |
Details
The kernel version of canonical correlation analysis.
Kernel Canonical Correlation Analysis (KCCA) is a non-linear extension
of CCA. Given two random variables, KCCA aims at extracting the
information which is shared by the two random variables. More
precisely given and
the purpose of KCCA is to provide
nonlinear mappings
and
such that their
correlation is maximized.
Value
An S4 object containing the following slots:
kcor |
Correlation coefficients in feature space |
xcoef |
estimated coefficients for the |
ycoef |
estimated coefficients for the |
Author(s)
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
References
Malte Kuss, Thore Graepel
The Geometry Of Kernel Canonical Correlation Analysis
https://www.microsoft.com/en-us/research/publication/the-geometry-of-kernel-canonical-correlation-analysis/
See Also
Examples
## dummy data
x <- matrix(rnorm(30),15)
y <- matrix(rnorm(30),15)
kcca(x,y,ncomps=2)