kcca {kernlab}R Documentation

Kernel Canonical Correlation Analysis

Description

Computes the canonical correlation analysis in a feature space.

Usage

## S4 method for signature '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 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
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. For 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.
... adittional parameters for the kpca function

Details

The kernel version of canonical correlation analysis.

Value

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

Author(s)

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

References

Malte Kuss, Thore Graepel
The Geometry Of Kernel Canonical Correlation Analysis
http://www.kyb.tuebingen.mpg.de/publications/pdfs/pdf2233.pdf

See Also

cancor kpca

Examples



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