| kernelMatrix {kernlab} | R Documentation |
kernelMatrix calculates the kernel matrix K_{ij} = k(x_i,x_j) or K_{ij} =
k(x_i,y_j).
kernelPol computes the quadratic kernel expression H = z_i z_j
k(x_i,x_j), H = z_i k_j k(x_i,y_j).
kernelMult calculates the kernel expansion f(x_i) =
sum_{i=1}^m z k(x_i,x_j)
kernelFast computes the kernel matrix this function identical
to kernelMatrix, except that it also requires the squared
norm of the first argument as additional input.
## S4 method for signature 'kernel': kernelMatrix(kernel, x, y = NULL) ## S4 method for signature 'kernel': kernelPol(kernel, x, y = NULL, z, k = NULL) ## S4 method for signature 'kernel': kernelMult(kernel, x, y = NULL, z, blocksize = 256) ## S4 method for signature 'kernel': kernelFast(kernel, x, y, a)
kernel |
the kernel function to be used to calculate the kernel
matrix.
This has to be a function of class kernel, i.e. either one of the build in
kernel functions or a fnction taking two vector arguments and returning a scalar. |
x |
a data matrix to be used to calculate the kernel matrix |
y |
second data matrix to calculate the kernel matrix |
z |
a suitable vector or matrix |
k |
a suitable vector or matrix |
a |
the squared norm of x e.g. rowSums(x^2) |
blocksize |
the kernel expansion computations are done block wise
to avoid storing the kernel matrix into memory. blocksize
defines the size of the computational blocks. |
Common functions used during kernel based computations.
The kernel 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 initialized by using the following
functions:
rbfdot Radial Basis kernel function
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 the Spline kernel
kernelFast is mainly used in situations where colums of the
kernel matrix are computed per invocation. In these cases,
evaluating the norm of each row-entry over and over again would
cause significant computational overhead.
kernelMatrix returns a symmetric diagonal semi-definite matrix.
kernelPol returns a matrix.
kernelMult usually returns a one-column matrix.
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
rbfdot, polydot,
tanhdot, vanilladot
## use the spam data data(spam) dt <- as.matrix(spam[c(10:20,3000:3010),-58]) ## initialize kernel function rbf <- rbfdot(sigma = 0.05) rbf ## calculate kernel matrix kernelMatrix(rbf, dt) yt <- as.matrix(as.integer(spam[c(10:20,3000:3010),58])) yt[yt==2] <- -1 ## calculate the quadratic kernel expression kernelPol(rbf, dt, ,yt) ## calculate the kernel expansion kernelMult(rbf, dt, ,yt)