kernelMatrix {svcR}R Documentation

Computation of clustering model by support vector machine

Description

This function implements three kinds of kernel to build a kernal matrix: Euclidian, Radial-Base and Exponential.

Usage

## S4 method for signature 'vector':
kernelMatrix.compute(x, 
          SymMat        = 1,
          q             = 1,
          ncol          = 2,
          nlin          = 2,
          KernChoice    = 1)

Arguments

x data matrix with a vector format
SymMat indicator to know if matrix is symetrical or not
q q parameter of the model
ncol number of columns of the data matrix
nlin number of lines of the data matrix
KernChoice option taking value "KernLinear" (Euclidian) or "KernGaussian" (RBF) or "KernGaussianDist" (Exponential) or "KernDist" (Matrix data as Kernel value)

Value

An S4 object of class kernelMatrix The object is the svc model along with the slots :

matrixKernel kernel matrix with a vector format
matrixK kernel matrix with a matrix format


slots can not be accessed.

Author(s)

Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr

References

N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf

Examples


## exemple with iris data

fmc = findSvcModel.Test(); 

getMatriceK(fmc); # kernel matrix


[Package svcR version 1.6.3 Index]