Deriving the stellar labels of LAMOST spectra with Stellar LAbel Machine
  (SLAM)
  The LAMOST survey has provided 9 million spectra in its Data Release 5 (DR5)
at R$\sim$1800. Extracting precise stellar labels is crucial for such a large
sample. In this paper, we report the implementation of the Stellar LAbel
Machine (SLAM), which is a data-driven method based on Support Vector
Regression (SVR), a robust non-linear regression technique. Thanks to the
capability to model highly non-linear problems with SVR, SLAM generally can
derive stellar labels over a wide range of spectral types. This gives it a
unique capability compared to other popular data-driven methods. To illustrate
this capability, we test the performance of SLAM on stars ranging from
Teff$\sim$4000 to $\sim$8000 K trained on LAMOST spectra and stellar labels. At
g-band signal-to-noise ratio (SNRg) higher than 100, the random uncertainties
of Teff, logg and [Fe/H] are 50 K, 0.09 dex, and 0.07 dex, respectively. We
then set up another SLAM model trained by APOGEE and LAMOST common stars to
demonstrate its capability of dealing with high dimensional problems. The
spectra are from LAMOST DR5 and the stellar labels of the training set are from
APOGEE DR15, including Teff, logg, [M/H],[$\alpha$/M], [C/M], and [N/M]. The
cross-validated scatters at SNRg$\sim$100 are 49 K, 0.10 dex, 0.037 dex,0.026
dex, 0.058 dex, and 0.106 dex for these parameters, respectively. This
performance is at the same level as other up-to-date data-driven models. As a
byproduct, we also provide the latest catalog of $\sim$1 million LAMOST DR5 K
giant stars with SLAM-predicted stellar labels in this work.
