csi-class {kernlab} | R Documentation

## Class "csi"

### Description

The reduced Cholesky decomposition object

### Objects from the Class

Objects can be created by calls of the form `new("csi", ...)`. or by calling the `csi` function.

### Slots

`.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

predgain

  Object of class `"vector"` contains the predicted gain before adding each column

truegain

  Object of class `"vector"` contains the actual gain after adding each column

Q

  Object of class `"matrix"` contains Q from the QR decomposition of the kernel matrix

R

  Object of class `"matrix"` contains R from the QR decomposition of the kernel matrix

### Extends

Class `"matrix"`, directly.

### Methods

diagresidues

  `signature(object = "csi")`: returns the diagonial residues

maxresiduals

  `signature(object = "csi")`: returns the maximum residues

pivots

  `signature(object = "csi")`: returns the pivots performed

predgain

  `signature(object = "csi")`: returns the predicted gain before adding each column

truegain

  `signature(object = "csi")`: returns the actual gain after adding each column

Q

  `signature(object = "csi")`: returns Q from the QR decomposition of the kernel matrix

R

  `signature(object = "csi")`: returns R from the QR decomposition of the kernel matrix

### Author(s)

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

### See Also

`csi`, `inchol-class`

### Examples

    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,]

[Package kernlab version 0.9-27 Index]