Computes the canonical correlation analysis in feature space.
# S4 method for matrix
kcca(x, y, kernel="rbfdot", kpar=list(sigma=0.1),
gamma = 0.1, ncomps = 10, ...)
An S4 object containing the following slots:
Correlation coefficients in feature space
estimated coefficients for the x
variables in the
feature space
estimated coefficients for the y
variables in the
feature space
a matrix containing data index by row
a matrix containing data index by row
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:
rbfdot
Radial Basis kernel function "Gaussian"
polydot
Polynomial kernel function
vanilladot
Linear kernel function
tanhdot
Hyperbolic tangent kernel function
laplacedot
Laplacian kernel function
besseldot
Bessel kernel function
anovadot
ANOVA RBF kernel function
splinedot
Spline kernel
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.
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 :
sigma
inverse kernel width for the Radial Basis
kernel function "rbfdot" and the Laplacian kernel "laplacedot".
degree, scale, offset
for the Polynomial kernel "polydot"
scale, offset
for the Hyperbolic tangent kernel
function "tanhdot"
sigma, order, degree
for the Bessel kernel "besseldot".
sigma, degree
for the ANOVA kernel "anovadot".
Hyper-parameters for user defined kernels can be passed through the kpar parameter as well.
regularization parameter (default : 0.1)
number of canonical components (default : 10)
additional parameters for the kpca
function
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
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 \(x\) and \(y\) the purpose of KCCA is to provide nonlinear mappings \(f(x)\) and \(g(y)\) such that their correlation is maximized.
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/
cancor
, kpca
, kfa
, kha
## dummy data
x <- matrix(rnorm(30),15)
y <- matrix(rnorm(30),15)
kcca(x,y,ncomps=2)
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