  A scheme for estimating atmospheric parameters T$_{eff}$, log$~g$, and [Fe/H]
is proposed on the basis of Least Absolute Shrinkage and Selection Operator
(LASSO) algorithm and Haar wavelet. The proposed scheme consists of three
processes. A spectrum is decomposed using the Haar wavelet transform and
low-frequency components at the fourth level are considered as candidate
features. Then, spectral features from the candidate features are detected
using the LASSO algorithm to estimate the atmospheric parameters. Finally,
atmospheric parameters are estimated from the extracted spectral features using
the support-vector regression (SVR) method. The proposed scheme was evaluated
using three sets of stellar spectra respectively from Sloan Digital Sky Survey
(SDSS), Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), and
Kurucz's model, respectively. The mean absolute errors are as follows: for
40~000 SDSS spectra, 0.0062 dex for log~T$_{eff}$ (85.83 K for T$_{eff}$),
0.2035 dex for log$~g$ and 0.1512 dex for [Fe/H]; for 23963 LAMOST spectra,
0.0074 dex for log~T$_{eff}$ (95.37 K for T$_{eff}$), 0.1528 dex for log~$g$,
and 0.1146 dex for [Fe/H]; and for 10469 synthetic spectra, 0.0010 dex for log
T$_{eff}$(14.42K for T$_{eff}$), 0.0123 dex for log~$g$, and 0.0125 dex for
[Fe/H].
