The function estimates the relevant dimension in feature space by fitting a twocomponent model. It's also able to calculate a denoised version of the labels and to estimate the noise level in the data set.
1 2 3 4 5 6 7 
K 
kernel matrix of the inputs (e.g. rbf kernel matrix) 
y 
label vector which contains the label for each data point 
est_y 
set this to TRUE if you want a denoised version of the labels 
alldim 
if this is TRUE denoised labels for all dimensions are calculated (instead of only for relevant dimension) 
est_noise 
set this to TRUE if you want an estimated noise level 
regression 
only interesting if one of 
nmse 
only interesting if 
dim_rest 
percantage of leading dimensions to which the search for the relevant dimensions should be restricted. This is needed due to numerical instabilities. 0.5 should be a good choice in most cases (and is also the default value) 
If est_noise
or alldim
are TRUE, a denoised version of the labels for the relevant dimension
will be returned even if est_y
is FALSE (so e.g. if you want denoised labels and noise approximation
it is enough to set est_noise
to TRUE).
rd 
estimated relevant dimension 
err 
negative loglikelihood for each dimension (the position of the minimum is the relevant dimension) 
yh 
only returned if 
Yh 
only returned if 
noise 
only returned if 
kpc 
kernel pca coefficients 
eigvec 
eigenvectors of the kernel matrix 
eigval 
eigenvalues of the kernel matrix 
tcm 
always TRUE; used to tell other functions that tcm method was used 
Jan Saputra Mueller
M. L. Braun, J. M. Buhmann, K. R. Mueller (2008) \_On Relevant Dimensions in Kernel Feature Spaces\_
rde
, rde_loocv
, estnoise
,
isregression
, rbfkernel
, polykernel
, drawkpc
1 2 3 4 5 6 7 8  ## example with sinc data
d < sincdata(100, 0.1) # generate sinc data
K < rbfkernel(d$X) # calculate rbf kernel matrix
# rde, return also denoised labels and noise
r < rde_tcm(K, d$y, est_y = TRUE, est_noise = TRUE)
r$rd # estimated relevant dimension
r$noise # estimated noise
drawkpc(r) # draw kernel pca coefficients

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