rde_tcm {rdetools} | R Documentation |
The function estimates the relevant dimension in feature space by fitting a two-component model. It's also able to calculate a denoised version of the labels and to estimate the noise level in the data set.
rde_tcm(K, y, est_y = FALSE, alldim = FALSE, est_noise = FALSE, regression = FALSE, nmse = TRUE, dim_rest = 0.5)
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 est_y , alldim , est_noise is TRUE. Set this
to TRUE if you want to force the function to handle the data as data for
a regression problem. If you leave this FALSE, the function will try to determine
itself whether this is a classification or regression problem. |
nmse |
only interesting if est_noise is TRUE and the function is handling the data as data
of a regression problem. If you leave this TRUE, the normalized mean squared error is used
for estimating the noise level, otherwise the conventional mean squared error. |
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 log-likelihood for each dimension (the position of the minimum is the relevant dimension) |
yh |
only returned if est_y , alldim or est_noise is TRUE, contains the denoised labels |
Yh |
only returned if alldim is TRUE, matrix with denoised labels for each dimension in each column |
noise |
only returned if est_noise is TRUE, contains the estimated noise level |
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
## 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