computeKernelProjection: computeKernelProjection

Description

Perform the semi-supervised Kernel PCA projection (see Bishop, 2006) directly from a data set, using the semi-supervised kernel transformation method in (Bruneau and Otjacques, 2012). Somehow combines computeCompositeKernel and computeProjectionFromKernel.

Usage

computeKernelProjection(data, dims=2, alpha=3, method="neighbors", pgaussian=TRUE)

Arguments

data

n x (d+1) matrix of d-dimensional row-elements. Last matrix column is a label specification in 1..n (NA if element is unlabelled)

dims

number of dimensions to which the projection is restricted.

alpha

parametrizes the kernel matrix transformation (see Bruneau and Otjacques, 2012)

method

transformation method, being either "simple" or "neighbors" (see Bruneau and Otjacques, 2012)

pgaussian

if TRUE, use the p-gaussian kernel function (see Francois et al. 2005). Else use the classic gaussian kernel, ie p-gaussian function with p=2 and sigma set to the maximal pairwise distance between elements of the data set.

Value

n x dims matrix of row-elements.

References

Bishop, C. M. (2006) Pattern recognition and machine learning. Springer. Bruneau, P. and Otjacques, B. (2012) Including semi-supervision in a kernel matrix, with a view to interactive visual clustering. Tech Report hal-00751407, CRP Gabriel Lippmann. Francois, D., Wertz, V. and Verleysen, M. (2005) About the locality of kernels in high-dimensional spaces. International Symposium on Applied Stochastic Models and Data Analysis. 238-245.

Examples

Run this code

data(iris)
irisdat <- as.matrix(cbind(iris[,1:4], rep(NA, 150)))
irisdat[sample(1:150,4),5] <- 1:4
proj <- computeKernelProjection(irisdat)