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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 threeprocesses. A spectrum is decomposed using the Haar wavelet transform andlow-frequency components at the fourth level are considered as candidatefeatures. Then, spectral features from the candidate features are detectedusing the LASSO algorithm to estimate the atmospheric parameters. Finally,atmospheric parameters are estimated from the extracted spectral features usingthe support-vector regression (SVR) method. The proposed scheme was evaluatedusing three sets of stellar spectra respectively from Sloan Digital Sky Survey(SDSS), Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), andKurucz's model, respectively. The mean absolute errors are as follows: for40~000 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 logT$_{eff}$(14.42K for T$_{eff}$), 0.0123 dex for log~$g$, and 0.0125 dex for[Fe/H].

Estimating stellar atmospheric parameters based on LASSO and support-vector regression