
rde(K, y, est_y = FALSE, alldim = FALSE, est_noise = FALSE, regression = FALSE, nmse = TRUE, dim_rest = 0.5, tcm = TRUE)
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.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.est_y
, alldim
or est_noise
is TRUE, contains the denoised labelsalldim
is TRUE, matrix with denoised labels for each dimension in each columnest_noise
is TRUE, contains the estimated noise levelest_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).
rde_loocv
, rde_tcm
, estnoise
,
isregression
, rbfkernel
, polykernel
, drawkpc
## example with sinc data using tcm algorithm
d <- sincdata(100, 0.1) # generate sinc data
K <- rbfkernel(d$X) # calculate rbf kernel matrix
# rde, return also denoised labels and noise, fit tcm
r <- rde(K, d$y, est_y = TRUE, est_noise = TRUE)
r$rd # estimated relevant dimension
r$noise # estimated noise
drawkpc(r) # draw kernel pca coefficients
## example with sinc data using loo-cv algorithm
d <- sincdata(100, 0.1) # generate sinc data
K <- rbfkernel(d$X) # calculate rbf kernel matrix
# rde, return also denoised labels and noise
r <- rde(K, d$y, est_y = TRUE, est_noise = TRUE, tcm = FALSE)
r$rd # estimated relevant dimension
r$noise # estimated noise
drawkpc(r) # draw kernel pca coefficients
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