  We present a method for deriving stellar fundamental parameters. It is based
on a regularized sliced inverse regression (RSIR). We first tested it on noisy
synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously
their atmospheric fundamental parameters: Teff, log g, [M/H] and vsini.
Different learning databases were calculated using a range of sampling in Teff,
log g, vsini, and [M/H]. Combined with a principal component analysis (PCA)
nearest neighbors (NN) search, the size of the learning database is reduced. A
Tikhonov regularization is applied, given the ill-conditioning of SIR. For all
spectral types, decreasing the size of the learning database allowed us to
reach internal accuracies better than PCA-based NN-search using larger learning
databases. For each analyzed parameter, we have reached internal errors that
are smaller than the sampling step of the parameter. We have also applied the
technique to a sample of observed FGK and A stars. For a selection of well
studied stars, the inverted parameters are in agreement with the ones derived
in previous studies. The RSIR inversion technique, complemented with PCA
pre-processing proves to be efficient in estimating stellar parameters of A, F,
G, and K stars.
