  The Optical Gravitational Lensing Experiment (OGLE) continuously monitors
hundreds of thousands of eclipsing binaries in the field of galactic bulge and
the Magellanic Clouds. These objects have been classified into main
morphological sub-classes, such as contact, non-contact, ellipsoidal and
cataclysmic variables, both by matching the light curves with predefined
templates and visual inspections. Here we present the result of a machine
learned automatic classification based on the morphology of light curves
inspired by the classification of eclipsing binaries observed by the original
Kepler mission. We similarly use a dimensionality reduction technique, the
locally linear embedding to map the high dimension of the data set into a low
dimensional embedding parameter space, while keeping the local geometry and the
similarities of the neighbouring data points. After three consecutive steps, we
assign one parameter to each binary star, which well scales with the
"detachness", i.e. the sum of the relative radii of the components. This value
is in good agreement with the morphology types listed in the OGLE catalog and,
along with the orbital periods, can be used to filter any morphological
sub-types based on the similarity of light curves. Our open-source pipeline can
be applied in a fully automatic way to any other large data set to classify
binary stars.
