Class "csi"
The reduced Cholesky decomposition object
Objects can be created by calls of the form new("csi", ...).
or by calling the csi function.
.Data:Object of class "matrix" contains
the decomposed matrix
pivots:Object of class "vector" contains
the pivots performed
diagresidues:Object of class "vector" contains
the diagonial residues
maxresiduals:Object of class "vector" contains
the maximum residues
Object of class "vector" contains
the predicted gain before adding each column
Object of class "vector" contains
the actual gain after adding each column
Object of class "matrix" contains
Q from the QR decomposition of the kernel matrix
Object of class "matrix" contains
R from the QR decomposition of the kernel matrix
Class "matrix", directly.
signature(object = "csi"): returns
the diagonial residues
signature(object = "csi"): returns
the maximum residues
signature(object = "csi"): returns
the pivots performed
signature(object = "csi"): returns
the predicted gain before adding each column
signature(object = "csi"): returns
the actual gain after adding each column
signature(object = "csi"): returns
Q from the QR decomposition of the kernel matrix
signature(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,]
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