A catalogue of 323 cataclysmic variables from LAMOST DR6
  In this work, we present a catalog of cataclysmic variables (CVs) identified
from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a
novel machine-learning algorithm called UMAP to screen out a total of 169,509
H$\alpha$-emission spectra, and obtain a classification accuracy of the
algorithm of over 99.6$\%$ from the cross-validation set. We then apply the
template matching program PyHammer v2.0 to the LAMOST spectra to obtain the
optimal spectral type with metallicity, which helps us identify the
chromospherically active stars and potential binary stars from the 169,509
spectra. After visually inspecting all the spectra, we identify 323 CV
candidates from the LAMOST database, among them 52 objects are new. We further
discuss the new CV candidates in subtypes based on their spectral features,
including five DN subtype during outbursts, five NL subtype and four magnetic
CVs (three AM Her type and one IP type). We also find two CVs that have been
previously identified by photometry, and confirm their previous classification
by the LAMOST spectra.
