csi
function in ## S3 method for class 'matrix':
csi(x, y, kernel="rbfdot", kpar=list(sigma=0.1), rank,
centering = TRUE, kappa = 0.99 ,delta = 40 ,tol = 1e-5)
kernel
,
which computes the inner product in feature space between two
vector arguments. kernlab provides the most popular kersigma
inverse kernel width for the Radial BasisTRUE
centering is performed (default: TRUE)object@slot
or by accessor functions with the same name
(e.g., pivots(object))
csi
uses the class labels, or regression responses to compute a
more apropriate aproximation for the problem at hand considering the
aditional information from the response variable.[object Object],[object Object],[object Object]
inchol
, chol
, csi-class
## 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,]