  Large-scale and deep sky survey missions are rapidly collecting a large
amount of stellar spectra, which necessitate the estimation of atmospheric
parameters directly from spectra and makes it feasible to statistically
investigate latent principles in a large dataset. We present a technique for
estimating parameters $T_{eff}$, log$~g$ and [Fe/H] from stellar spectra. With
this technique, we first extract features from stellar spectra using the LASSO
algorithm; then, the parameters are estimated from the extracted features using
the SVR. On a subsample of 20~000 stellar spectra from SDSS with reference
parameters provided by SDSS/SEGUE Pipeline SSPP, estimation consistency are
0.007458 dex for log$~T_{eff}$ (101.609921 K for $T_{eff}$), 0.189557 dex for
log$~g$ and 0.182060 for [Fe/H], where the consistency is evaluated by mean
absolute error. Prominent characteristics of the proposed scheme are
sparseness, locality, and physical interpretability. In this work, every
spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength
positions are detected respectively for estimating $T_{eff}$, log$~g$ and
[Fe/H]. It is shown that the positions are related to typical lines of stellar
spectra. This characteristic is important in investigating physical indications
from analysis results. Then, stellar spectra can be described by the individual
fluxes on the detected positions (PD) or local integration of fluxes near them
(LI). The abovementioned consistency is the result based on features described
by LI. If features are described by PD, consistency are 0.009092 dex for
log$~T_{eff}$ (124.545075 K for $T_{eff}$), 0.198928 dex for log$~g$, and
0.206814 dex for [Fe/H].
