Description Usage Arguments Value Author(s) References See Also Examples
Compute the kernel PCA projection from an input kernel matrix.
1 | computeProjectionFromKernel(kernel, dims=2, eigentype=c("basic", "irlba"))
|
kernel |
n x n kernel matrix |
dims |
number of output dimensions for the projection. Cannot exceed n. |
eigentype |
Indicates the eigendecomposition routine that should be used, either the standard ("basic"), or an optimization for the extraction of a few major eigenpairs ("irlba") |
n x dims matrix of the projected data.
Pierrick Bruneau
Bishop, C. M. (2006) Pattern recognition and machine learning. Springer.
computeStandardKernel
1 2 3 4 | data(iris)
irisdat <- as.matrix(iris[,1:4])
kernel <- computeKernel(irisdat)
proj <- computeProjectionFromKernel(kernel)
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