| csi-class {kernlab} | R Documentation |
The reduced Cholesky decomposition object
Objects can be created by calls of the form new("csi", ...).
or by calling the csi function.
.Data:"matrix" contains
the decomposed matrixpivots:"vector" contains
the pivots performeddiagresidues:"vector" contains
the diagonial residuesmaxresiduals:"vector" contains
the maximum residues"vector" contains
the predicted gain before adding each column"vector" contains
the actual gain after adding each column"matrix" contains
Q from the QR decomposition of the kernel matrix"matrix" contains
R from the QR decomposition of the kernel matrix
Class "matrix", directly.
signature(object = "csi"): returns
the diagonial residuessignature(object = "csi"): returns
the maximum residuessignature(object = "csi"): returns
the pivots performedsignature(object = "csi"): returns
the predicted gain before adding each columnsignature(object = "csi"): returns
the actual gain after adding each columnsignature(object = "csi"): returns
Q from the QR decomposition of the kernel matrixsignature(object = "csi"): returns
R from the QR decomposition of the kernel matrix
Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at
data(iris) ## create multidimensional y matrix yind <- t(matrix(1:3,3,150)) ymat <- matrix(0, 150, 3) ymat[yind==as.integer(iris[,5])] <- 1 datamatrix <- as.matrix(iris[,-5]) # initialize kernel function rbf <- rbfdot(sigma=0.1) rbf Z <- csi(datamatrix,ymat, kernel=rbf, rank = 30) dim(Z) pivots(Z) # calculate kernel matrix K <- crossprod(t(Z)) # difference between approximated and real kernel matrix (K - kernelMatrix(kernel=rbf, datamatrix))[6,]