  To study the quality of stellar spectra of the Large Sky Area Multi-Object
Fiber Spectroscopic Telescope (LAMOST) and the correctness of the corresponding
stellar parameters derived by the LASP (LAMOST Stellar Parameter Pipeline), the
outlier analysis method is applied to the archived AFGK stars in the fifth data
release (DR5) of LAMOST. The outlier factor is defined in order to sort more
than 3 million stellar spectra selected from the DR5 Stellar Parameter catalog.
We propose an improved Local Outlier Factor (LOF) method based on Principal
Component Analysis and Monte Carlo to enable the computation of the LOF
rankings for randomly picked sub-samples that are computed in parallel by
multiple computers, and finally to obtain the outlier ranking of each spectrum
in the entire dataset. Totally 3,627 most outlier ranked spectra, around
one-thousandth of all spectra, are selected and clustered into 10 groups, and
the parameter density distribution of them conforms to the parameter
distribution of LAMOST DR5, which suggests that in the whole parameter space
the probability of bad spectra is uniformly distributed. By cross-matching the
3,627 spectra with APOGEE, we obtain 122 common ones. The published parameters
calculated from LASP agree with APOGEE for the 122 spectra although there are
bad pixels or bad flux calibrations in them. On the other hand, some outlier
spectra show strong nebular contamination warning the corresponding parameters
should be carefully used. A catalog and a spectral atlas of all the 3,627
outliers can be found at the link
http://paperdata.china-vo.org/LY_paper/dr5Outlier/dr5Outlier_resource.zip.
