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 _ { \texttt { 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 20Â 000 stellar spectra from SDSS with reference parameters provided by SDSS/SEGUE Pipeline SSPP , estimation consistency are 0.007458 dex for log ~ { } T _ { \texttt { eff } } ( 101.609921 K for T _ { \texttt { 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 _ { \texttt { 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 _ { \texttt { eff } } ( 124.545075 K for T _ { \texttt { eff } } ) , 0.198928 dex for log ~ { } g , and 0.206814 dex for [ Fe/H ] .