  We describe a scheme to extract linearly supporting (LSU) features from
stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$,
log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric
parameters can be accurately estimated from the extracted features through a
linear model. The successive steps of the process are as follow: first,
decompose the spectrum using a wavelet packet (WP) and represent it by the
derived decomposition coefficients; second, detect representative spectral
features from the decomposition coefficients using the proposed method Least
Absolute Shrinkage and Selection Operator (LARS)$_{bs}$; third, estimate the
atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H] from the detected
features using a linear regression method. One prominent characteristic of this
scheme is its ability to evaluate quantitatively the contribution of each
detected feature to the atmospheric parameter estimate and also to trace back
the physical significance of that feature. This work also shows that the
usefulness of a component depends on both wavelength and frequency. The
proposed scheme has been evaluated on both real spectra from the Sloan Digital
Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF
models. On real spectra, we extracted 23 features to estimate $T_{eff}$, 62
features for log$~g$, and 68 features for [Fe/H]. Test consistencies between
our estimates and those provided by the Spectroscopic Sarameter Pipeline of
SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log$~T_{eff}$
(83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For
the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$
(32 K for $T_{eff}$), 0.0337 dex for log$~g$, and 0.0268 dex for [Fe/H].
